1
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D'Agostino D, Ilievski I, Shoemaker CA. Learning active subspaces and discovering important features with Gaussian radial basis functions neural networks. Neural Netw 2024; 176:106335. [PMID: 38733793 DOI: 10.1016/j.neunet.2024.106335] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2023] [Revised: 02/21/2024] [Accepted: 04/22/2024] [Indexed: 05/13/2024]
Abstract
Providing a model that achieves a strong predictive performance and is simultaneously interpretable by humans is one of the most difficult challenges in machine learning research due to the conflicting nature of these two objectives. To address this challenge, we propose a modification of the radial basis function neural network model by equipping its Gaussian kernel with a learnable precision matrix. We show that precious information is contained in the spectrum of the precision matrix that can be extracted once the training of the model is completed. In particular, the eigenvectors explain the directions of maximum sensitivity of the model revealing the active subspace and suggesting potential applications for supervised dimensionality reduction. At the same time, the eigenvectors highlight the relationship in terms of absolute variation between the input and the latent variables, thereby allowing us to extract a ranking of the input variables based on their importance to the prediction task enhancing the model interpretability. We conducted numerical experiments for regression, classification, and feature selection tasks, comparing our model against popular machine learning models, the state-of-the-art deep learning-based embedding feature selection techniques, and a transformer model for tabular data. Our results demonstrate that the proposed model does not only yield an attractive prediction performance compared to the competitors but also provides meaningful and interpretable results that potentially could assist the decision-making process in real-world applications. A PyTorch implementation of the model is available on GitHub at the following link.1.
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Affiliation(s)
- Danny D'Agostino
- National University of Singapore, Department of Industrial Systems Engineering and Management, Singapore.
| | - Ilija Ilievski
- National University of Singapore, Department of Industrial Systems Engineering and Management, Singapore
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2
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Chandra RS, Ying GS. Predicting Visual Acuity Responses to Anti-VEGF Treatment in the Comparison of Age-related Macular Degeneration Treatments Trials Using Machine Learning. Ophthalmol Retina 2024; 8:419-430. [PMID: 38008218 PMCID: PMC11070304 DOI: 10.1016/j.oret.2023.11.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Revised: 11/06/2023] [Accepted: 11/20/2023] [Indexed: 11/28/2023]
Abstract
PURPOSE To evaluate multiple machine learning (ML) models for predicting 2-year visual acuity (VA) responses to anti-vascular endothelial growth factor (anti-VEGF) treatment in the Comparison of Age-related Macular Degeneration (AMD) Treatments Trials (CATT) for patients with neovascular AMD (nAMD). DESIGN Secondary analysis of public data from a randomized clinical trial. PARTICIPANTS A total of 1029 CATT participants who completed 2 years of follow-up with untreated active nAMD and baseline VA between 20/25 and 20/320 in the study eye. METHODS Five ML models (support vector machine, random forest, extreme gradient boosting, multilayer perceptron neural network, and lasso) were applied to clinical and image data from baseline and weeks 4, 8, and 12 for predicting 4 VA outcomes (≥ 15-letter VA gain, ≥ 15-letter VA loss, VA change from baseline, and actual VA) at 2 years. The CATT data from 1029 participants were randomly split for training (n = 717), from which the models were trained using 10-fold cross-validation, and for final validation on a test data set (n = 312). MAIN OUTCOME MEASURES Performances of ML models were assessed by R2 and mean absolute error (MAE) for predicting VA change from baseline and actual VA at 2 years, by the area under the receiver operating characteristic curve (AUC) for predicting ≥ 15-letter VA gain and loss from baseline. RESULTS Using training data up to week 12, the ML models from cross-validation achieved mean R2 of 0.24 to 0.29 (MAE = 9.1-9.8 letters) for predicting VA change and 0.37 to 0.41 (MAE = 9.3-10.2 letters) for predicting actual VA at 2 years. The mean AUCs for predicting ≥ 15-letter VA gain and loss at 2 years was 0.84 to 0.85 and 0.58 to 0.73, respectively. In final validation on the test data set up to week 12, the models had an R2 of 0.33 to 0.38 (MAE = 8.9-9.9 letters) for predicting VA change, an R2 of 0.37 to 0.45 (MAE = 8.8-10.2 letters) for predicting actual VA at 2 years, and AUCs of 0.85 to 0.87 and 0.67 to 0.79 for predicting ≥ 15-letter VA gain and loss, respectively. CONCLUSIONS Machine learning models have the potential to predict 2-year VA response to anti-VEGF treatment using clinical and imaging features from the loading dose phase, which can aid in decision-making around treatment protocols for patients with nAMD. FINANCIAL DISCLOSURE(S) The author(s) have no proprietary or commercial interest in any materials discussed in this article.
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Affiliation(s)
- Rajat S Chandra
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Gui-Shuang Ying
- Department of Ophthalmology, Center for Preventive Ophthalmology and Biostatistics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania.
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3
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Zhu C, Bamidele EA, Shen X, Zhu G, Li B. Machine Learning Aided Design and Optimization of Thermal Metamaterials. Chem Rev 2024; 124:4258-4331. [PMID: 38546632 PMCID: PMC11009967 DOI: 10.1021/acs.chemrev.3c00708] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2023] [Revised: 01/31/2024] [Accepted: 02/08/2024] [Indexed: 04/11/2024]
Abstract
Artificial Intelligence (AI) has advanced material research that were previously intractable, for example, the machine learning (ML) has been able to predict some unprecedented thermal properties. In this review, we first elucidate the methodologies underpinning discriminative and generative models, as well as the paradigm of optimization approaches. Then, we present a series of case studies showcasing the application of machine learning in thermal metamaterial design. Finally, we give a brief discussion on the challenges and opportunities in this fast developing field. In particular, this review provides: (1) Optimization of thermal metamaterials using optimization algorithms to achieve specific target properties. (2) Integration of discriminative models with optimization algorithms to enhance computational efficiency. (3) Generative models for the structural design and optimization of thermal metamaterials.
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Affiliation(s)
- Changliang Zhu
- Department
of Materials Science and Engineering, Southern
University of Science and Technology, Shenzhen 518055, P.R. China
| | - Emmanuel Anuoluwa Bamidele
- Materials
Science and Engineering Program, University
of Colorado, Boulder, Colorado 80309, United States
| | - Xiangying Shen
- Department
of Materials Science and Engineering, Southern
University of Science and Technology, Shenzhen 518055, P.R. China
| | - Guimei Zhu
- School
of Microelectronics, Southern University
of Science and Technology, Shenzhen 518055, P.R. China
| | - Baowen Li
- Department
of Materials Science and Engineering, Southern
University of Science and Technology, Shenzhen 518055, P.R. China
- School
of Microelectronics, Southern University
of Science and Technology, Shenzhen 518055, P.R. China
- Department
of Physics, Southern University of Science
and Technology, Shenzhen 518055, P.R. China
- Shenzhen
International Quantum Academy, Shenzhen 518048, P.R. China
- Paul M. Rady
Department of Mechanical Engineering and Department of Physics, University of Colorado, Boulder 80309, United States
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4
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Sniatynski MJ, Shepherd JA, Wilkens LR, Hsu DF, Kristal BS. The DIRAC framework: Geometric structure underlies roles of diversity and accuracy in combining classifiers. PATTERNS (NEW YORK, N.Y.) 2024; 5:100924. [PMID: 38487799 PMCID: PMC10935508 DOI: 10.1016/j.patter.2024.100924] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/25/2023] [Revised: 09/16/2023] [Accepted: 01/08/2024] [Indexed: 03/17/2024]
Abstract
Combining classification systems potentially improves predictive accuracy, but outcomes have proven impossible to predict. Similar to improving binary classification with fusion, fusing ranking systems most commonly increases Pearson or Spearman correlations with a target when the input classifiers are "sufficiently good" (generalized as "accuracy") and "sufficiently different" (generalized as "diversity"), but the individual and joint quantitative influence of these factors on the final outcome remains unknown. We resolve these issues. Building on our previous empirical work establishing the DIRAC (DIversity of Ranks and ACcuracy) framework, which accurately predicts the outcome of fusing binary classifiers, we demonstrate that the DIRAC framework similarly explains the outcome of fusing ranking systems. Specifically, precise geometric representation of diversity and accuracy as angle-based distances within rank-based combinatorial structures (permutahedra) fully captures their synergistic roles in rank approximation, uncouples them from the specific metrics of a given problem, and represents them as generally as possible.
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Affiliation(s)
- Matthew J. Sniatynski
- Division of Sleep and Circadian Disorders, Department of Medicine, Brigham and Women’s Hospital, Boston, MA 02115, USA
- Division of Sleep Medicine, Department of Medicine, Harvard Medical School, Boston, MA 02115, USA
| | - John A. Shepherd
- School of Medicine, University of California, San Francisco, San Francisco, CA, USA
| | - Lynne R. Wilkens
- University of Hawaii Cancer Center, University of Hawaii at Mānoa, Honolulu, HI, USA
| | - D. Frank Hsu
- Department of Computer and Information Science, Fordham University, New York, NY 10023, USA
| | - Bruce S. Kristal
- Division of Sleep and Circadian Disorders, Department of Medicine, Brigham and Women’s Hospital, Boston, MA 02115, USA
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5
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Zeng G, Hao X, Wang H, Li H, Gao F. Effects of geographical origin, vintage, and soil on stable isotopes and mineral elements in Ecolly grape berries for traceability. Food Chem 2024; 435:137646. [PMID: 37806197 DOI: 10.1016/j.foodchem.2023.137646] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2023] [Revised: 09/22/2023] [Accepted: 09/30/2023] [Indexed: 10/10/2023]
Abstract
Stable isotopes and multi-element profiles of grapes and corresponding soils from different origins and vintages were determined by IRMS and ICP-MS, respectively. Stable isotope ratios and multi-element contents show significant differences among distinct regions and vintages. Grapes and soils were separated using δ2H and δ18O according to regions and vintages. PCA and CA results further verified that multi-element profiles were influenced by origins and vintages. In particular, δ2H, δ18O, and 21 elements in grapes were correlated with those in soil. Redundancy and Spearman analyses revealed that the BCF values were related to the longitude, latitude, altitude, precipitation, and average temperature. RF shows better performance than PLS-DA for discriminating grape origins and vintages. K, Tb, Cs, δ2H, and Co were important variables in discriminating grape origins. These findings confirm that isotopic and elemental profiles depend on the origin, vintage, and soil, establishing a promising method to discriminate grape origins and vintages.
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Affiliation(s)
- Guihua Zeng
- School of Food Science and Technology, Shihezi University, Shihezi, Xinjiang 832000, China; College of Enology, Shaanxi Engineering Research Center for Viti-viniculture, Northwest A&F University, Yangling, Shaanxi 712100, China
| | - Xiaoyun Hao
- College of Enology, Shaanxi Engineering Research Center for Viti-viniculture, Northwest A&F University, Yangling, Shaanxi 712100, China; School of Chemical Engineering, Xi'an University, Xi'an, Shaanxi 710065, China
| | - Hua Wang
- College of Enology, Shaanxi Engineering Research Center for Viti-viniculture, Northwest A&F University, Yangling, Shaanxi 712100, China
| | - Hua Li
- College of Enology, Shaanxi Engineering Research Center for Viti-viniculture, Northwest A&F University, Yangling, Shaanxi 712100, China.
| | - Feifei Gao
- School of Food Science and Technology, Shihezi University, Shihezi, Xinjiang 832000, China; College of Enology, Shaanxi Engineering Research Center for Viti-viniculture, Northwest A&F University, Yangling, Shaanxi 712100, China.
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6
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Griffin C, Karn T, Apple B. Topological learning in multiclass data sets. Phys Rev E 2024; 109:024131. [PMID: 38491638 DOI: 10.1103/physreve.109.024131] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2023] [Accepted: 02/01/2024] [Indexed: 03/18/2024]
Abstract
We specialize techniques from topological data analysis to the problem of characterizing the topological complexity (as defined in the body of the paper) of a multiclass data set. As a by-product, a topological classifier is defined that uses an open subcovering of the data set. This subcovering can be used to construct a simplicial complex whose topological features (e.g., Betti numbers) provide information about the classification problem. We use these topological constructs to study the impact of topological complexity on learning in feedforward deep neural networks (DNNs). We hypothesize that topological complexity is negatively correlated with the ability of a fully connected feedforward deep neural network to learn to classify data correctly. We evaluate our topological classification algorithm on multiple constructed and open-source data sets. We also validate our hypothesis regarding the relationship between topological complexity and learning in DNN's on multiple data sets.
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Affiliation(s)
- Christopher Griffin
- Applied Research Laboratory, The Pennsylvania State University, University Park, Pennsylvania 16802, USA
| | - Trevor Karn
- School of Mathematics, University of Minnesota, Minneapolis, Minnesota 55455, USA
| | - Benjamin Apple
- Naval Surface Warfare Center Carderock, Bethesda Maryland 20817, USA
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7
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Chen LD, Caprio MA, Chen DM, Kouba AJ, Kouba CK. Enhancing predictive performance for spectroscopic studies in wildlife science through a multi-model approach: A case study for species classification of live amphibians. PLoS Comput Biol 2024; 20:e1011876. [PMID: 38354202 PMCID: PMC10898777 DOI: 10.1371/journal.pcbi.1011876] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Revised: 02/27/2024] [Accepted: 01/30/2024] [Indexed: 02/16/2024] Open
Abstract
Near infrared spectroscopy coupled with predictive modeling is a growing field of study for addressing questions in wildlife science aimed at improving management strategies and conservation outcomes for managed and threatened fauna. To date, the majority of spectroscopic studies in wildlife and fisheries applied chemometrics and predictive modeling with a single-algorithm approach. By contrast, multi-model approaches are used routinely for analyzing spectroscopic datasets across many major industries (e.g., medicine, agriculture) to maximize predictive outcomes for real-world applications. In this study, we conducted a benchmark modeling exercise to compare the performance of several machine learning algorithms in a multi-class problem utilizing a multivariate spectroscopic dataset obtained from live animals. Spectra obtained from live individuals representing eleven amphibian species were classified according to taxonomic designation. Seven modeling techniques were applied to generate prediction models, which varied significantly (p < 0.05) with regard to mean classification accuracy (e.g., support vector machine: 95.8 ± 0.8% vs. K-nearest neighbors: 89.3 ± 1.0%). Through the use of a multi-algorithm approach, candidate algorithms can be identified and applied to more effectively model complex spectroscopic data collected for wildlife sciences. Other key considerations in the predictive modeling workflow that serve to optimize spectroscopic model performance (e.g., variable selection and cross-validation procedures) are also discussed.
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Affiliation(s)
- Li-Dunn Chen
- Department of Biochemistry, Molecular Biology, Entomology, & Plant Pathology, Mississippi State University, Mississippi, United States of America
| | - Michael A. Caprio
- Department of Biochemistry, Molecular Biology, Entomology, & Plant Pathology, Mississippi State University, Mississippi, United States of America
| | - Devin M. Chen
- Department of Wildlife, Fisheries, & Aquaculture, Mississippi State University, Mississippi, United States of America
| | - Andrew J. Kouba
- Department of Wildlife, Fisheries, & Aquaculture, Mississippi State University, Mississippi, United States of America
| | - Carrie K. Kouba
- Department of Biochemistry, Molecular Biology, Entomology, & Plant Pathology, Mississippi State University, Mississippi, United States of America
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8
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Zhang Y, Li Q, Xin Y. Research on eight machine learning algorithms applicability on different characteristics data sets in medical classification tasks. Front Comput Neurosci 2024; 18:1345575. [PMID: 38356726 PMCID: PMC10864458 DOI: 10.3389/fncom.2024.1345575] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2023] [Accepted: 01/15/2024] [Indexed: 02/16/2024] Open
Abstract
With the vigorous development of data mining field, more and more algorithms have been proposed or improved. How to quickly select a data mining algorithm that is suitable for data sets in medical field is a challenge for some medical workers. The purpose of this paper is to study the comparative characteristics of the general medical data set and the general data sets in other fields, and find the applicability rules of the data mining algorithm suitable for the characteristics of the current research data set. The study quantified characteristics of the research data set with 26 indicators, including simple indicators, statistical indicators and information theory indicators. Eight machine learning algorithms with high maturity, low user involvement and strong family representation were selected as the base algorithms. The algorithm performances were evaluated by three aspects: prediction accuracy, running speed and memory consumption. By constructing decision tree and stepwise regression model to learn the above metadata, the algorithm applicability knowledge of medical data set is obtained. Through cross-verification, the accuracy of all the algorithm applicability prediction models is above 75%, which proves the validity and feasibility of the applicability knowledge.
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Affiliation(s)
- Yiyan Zhang
- School of Intelligent Manufacturing, Qingdao Huanghai University, Qingdao, China
| | - Qin Li
- School of Life Science, Beijing Institute of Technology, Beijing, China
| | - Yi Xin
- School of Life Science, Beijing Institute of Technology, Beijing, China
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9
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Valentin S, Kleinegesse S, Bramley NR, Seriès P, Gutmann MU, Lucas CG. Designing optimal behavioral experiments using machine learning. eLife 2024; 13:e86224. [PMID: 38261382 PMCID: PMC10805374 DOI: 10.7554/elife.86224] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2023] [Accepted: 11/19/2023] [Indexed: 01/24/2024] Open
Abstract
Computational models are powerful tools for understanding human cognition and behavior. They let us express our theories clearly and precisely and offer predictions that can be subtle and often counter-intuitive. However, this same richness and ability to surprise means our scientific intuitions and traditional tools are ill-suited to designing experiments to test and compare these models. To avoid these pitfalls and realize the full potential of computational modeling, we require tools to design experiments that provide clear answers about what models explain human behavior and the auxiliary assumptions those models must make. Bayesian optimal experimental design (BOED) formalizes the search for optimal experimental designs by identifying experiments that are expected to yield informative data. In this work, we provide a tutorial on leveraging recent advances in BOED and machine learning to find optimal experiments for any kind of model that we can simulate data from, and show how by-products of this procedure allow for quick and straightforward evaluation of models and their parameters against real experimental data. As a case study, we consider theories of how people balance exploration and exploitation in multi-armed bandit decision-making tasks. We validate the presented approach using simulations and a real-world experiment. As compared to experimental designs commonly used in the literature, we show that our optimal designs more efficiently determine which of a set of models best account for individual human behavior, and more efficiently characterize behavior given a preferred model. At the same time, formalizing a scientific question such that it can be adequately addressed with BOED can be challenging and we discuss several potential caveats and pitfalls that practitioners should be aware of. We provide code to replicate all analyses as well as tutorial notebooks and pointers to adapt the methodology to different experimental settings.
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Affiliation(s)
- Simon Valentin
- School of Informatics, University of EdinburghEdinburghUnited Kingdom
| | | | - Neil R Bramley
- Department of Psychology, University of EdinburghEdinburghUnited Kingdom
| | - Peggy Seriès
- School of Informatics, University of EdinburghEdinburghUnited Kingdom
| | - Michael U Gutmann
- School of Informatics, University of EdinburghEdinburghUnited Kingdom
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10
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Fu X, Suo H, Zhang J, Chen D. Machine-learning-guided Directed Evolution for AAV Capsid Engineering. Curr Pharm Des 2024; 30:811-824. [PMID: 38445704 DOI: 10.2174/0113816128286593240226060318] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2023] [Revised: 02/07/2024] [Accepted: 02/13/2024] [Indexed: 03/07/2024]
Abstract
Target gene delivery is crucial to gene therapy. Adeno-associated virus (AAV) has emerged as a primary gene therapy vector due to its broad host range, long-term expression, and low pathogenicity. However, AAV vectors have some limitations, such as immunogenicity and insufficient targeting. Designing or modifying capsids is a potential method of improving the efficacy of gene delivery, but hindered by weak biological basis of AAV, complexity of the capsids, and limitations of current screening methods. Artificial intelligence (AI), especially machine learning (ML), has great potential to accelerate and improve the optimization of capsid properties as well as decrease their development time and manufacturing costs. This review introduces the traditional methods of designing AAV capsids and the general steps of building a sequence-function ML model, highlights the applications of ML in the development workflow, and summarizes its advantages and challenges.
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Affiliation(s)
- Xianrong Fu
- School of Artificial Intelligence, Hangzhou Dianzi University, Hangzhou 310018, China
| | - Hairui Suo
- School of Artificial Intelligence, Hangzhou Dianzi University, Hangzhou 310018, China
| | - Jiachen Zhang
- School of Artificial Intelligence, Hangzhou Dianzi University, Hangzhou 310018, China
| | - Dongmei Chen
- School of Artificial Intelligence, Hangzhou Dianzi University, Hangzhou 310018, China
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11
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Fang C, Yang Z, Wassermann D, Li JR. A simulation-driven supervised learning framework to estimate brain microstructure using diffusion MRI. Med Image Anal 2023; 90:102979. [PMID: 37827109 DOI: 10.1016/j.media.2023.102979] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2023] [Revised: 09/13/2023] [Accepted: 09/22/2023] [Indexed: 10/14/2023]
Abstract
We propose a framework to train supervised learning models on synthetic data to estimate brain microstructure parameters using diffusion magnetic resonance imaging (dMRI). Although further validation is necessary, the proposed framework aims to seamlessly incorporate realistic simulations into dMRI microstructure estimation. Synthetic data were generated from over 1,000 neuron meshes converted from digital neuronal reconstructions and linked to their neuroanatomical parameters (such as soma volume and neurite length) using an optimized diffusion MRI simulator that produces intracellular dMRI signals from the solution of the Bloch-Torrey partial differential equation. By combining random subsets of simulated neuron signals with a free diffusion compartment signal, we constructed a synthetic dataset containing dMRI signals and 40 tissue microstructure parameters of 1.45 million artificial brain voxels. To implement supervised learning models we chose multilayer perceptrons (MLPs) and trained them on a subset of the synthetic dataset to estimate some microstructure parameters, namely, the volume fractions of soma, neurites, and the free diffusion compartment, as well as the area fractions of soma and neurites. The trained MLPs perform satisfactorily on the synthetic test sets and give promising in-vivo parameter maps on the MGH Connectome Diffusion Microstructure Dataset (CDMD). Most importantly, the estimated volume fractions showed low dependence on the diffusion time, the diffusion time independence of the estimated parameters being a desired property of quantitative microstructure imaging. The synthetic dataset we generated will be valuable for the validation of models that map between the dMRI signals and microstructure parameters. The surface meshes and microstructures parameters of the aforementioned neurons have been made publicly available.
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Affiliation(s)
- Chengran Fang
- INRIA Saclay, Equipe IDEFIX, UMA, ENSTA Paris, 828, Boulevard des Maréchaux, 91762 Palaiseau, France; INRIA Saclay, Equipe MIND, 1 Rue Honoré d'Estienne d'Orves, 91120 Palaiseau, France
| | - Zheyi Yang
- INRIA Saclay, Equipe IDEFIX, UMA, ENSTA Paris, 828, Boulevard des Maréchaux, 91762 Palaiseau, France
| | - Demian Wassermann
- INRIA Saclay, Equipe MIND, 1 Rue Honoré d'Estienne d'Orves, 91120 Palaiseau, France
| | - Jing-Rebecca Li
- INRIA Saclay, Equipe IDEFIX, UMA, ENSTA Paris, 828, Boulevard des Maréchaux, 91762 Palaiseau, France.
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12
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Demircioğlu A. Deep Features from Pretrained Networks Do Not Outperform Hand-Crafted Features in Radiomics. Diagnostics (Basel) 2023; 13:3266. [PMID: 37892087 PMCID: PMC10606594 DOI: 10.3390/diagnostics13203266] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2023] [Revised: 10/16/2023] [Accepted: 10/19/2023] [Indexed: 10/29/2023] Open
Abstract
In radiomics, utilizing features extracted from pretrained deep networks could result in models with a higher predictive performance than those relying on hand-crafted features. This study compared the predictive performance of models trained with either deep features, hand-crafted features, or a combination of these features in terms of the area under the receiver-operating characteristic curve (AUC) and other metrics. We trained models on ten radiological datasets using five feature selection methods and three classifiers. Our results indicate that models based on deep features did not show an improved AUC compared to those utilizing hand-crafted features (deep: AUC 0.775, hand-crafted: AUC 0.789; p = 0.28). Including morphological features alongside deep features led to overall improvements in prediction performance for all models (+0.02 gain in AUC; p < 0.001); however, the best model did not benefit from this (+0.003 gain in AUC; p = 0.57). Using all hand-crafted features in addition to the deep features resulted in a further overall improvement (+0.034 in AUC; p < 0.001), but only a minor improvement could be observed for the best model (deep: AUC 0.798, hand-crafted: AUC 0.789; p = 0.92). Furthermore, our results show that models based on deep features extracted from networks pretrained on medical data have no advantage in predictive performance over models relying on features extracted from networks pretrained on ImageNet data. Our study contributes a benchmarking analysis of models trained on hand-crafted and deep features from pretrained networks across multiple datasets. It also provides a comprehensive understanding of their applicability and limitations in radiomics. Our study shows, in conclusion, that models based on features extracted from pretrained deep networks do not outperform models trained on hand-crafted ones.
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Affiliation(s)
- Aydin Demircioğlu
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Hufelandstraße 55, 45147 Essen, Germany
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13
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van Breugel M, Fehrmann RSN, Bügel M, Rezwan FI, Holloway JW, Nawijn MC, Fontanella S, Custovic A, Koppelman GH. Current state and prospects of artificial intelligence in allergy. Allergy 2023; 78:2623-2643. [PMID: 37584170 DOI: 10.1111/all.15849] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2023] [Revised: 07/08/2023] [Accepted: 07/31/2023] [Indexed: 08/17/2023]
Abstract
The field of medicine is witnessing an exponential growth of interest in artificial intelligence (AI), which enables new research questions and the analysis of larger and new types of data. Nevertheless, applications that go beyond proof of concepts and deliver clinical value remain rare, especially in the field of allergy. This narrative review provides a fundamental understanding of the core concepts of AI and critically discusses its limitations and open challenges, such as data availability and bias, along with potential directions to surmount them. We provide a conceptual framework to structure AI applications within this field and discuss forefront case examples. Most of these applications of AI and machine learning in allergy concern supervised learning and unsupervised clustering, with a strong emphasis on diagnosis and subtyping. A perspective is shared on guidelines for good AI practice to guide readers in applying it effectively and safely, along with prospects of field advancement and initiatives to increase clinical impact. We anticipate that AI can further deepen our knowledge of disease mechanisms and contribute to precision medicine in allergy.
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Affiliation(s)
- Merlijn van Breugel
- Department of Pediatric Pulmonology and Pediatric Allergology, Beatrix Children's Hospital, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands
- Groningen Research Institute for Asthma and COPD (GRIAC), University Medical Center Groningen, University of Groningen, Groningen, the Netherlands
- MIcompany, Amsterdam, the Netherlands
| | - Rudolf S N Fehrmann
- Department of Medical Oncology, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands
| | | | - Faisal I Rezwan
- Human Development and Health, Faculty of Medicine, University of Southampton, Southampton, UK
- Department of Computer Science, Aberystwyth University, Aberystwyth, UK
| | - John W Holloway
- Human Development and Health, Faculty of Medicine, University of Southampton, Southampton, UK
- National Institute for Health and Care Research Southampton Biomedical Research Centre, University Hospitals Southampton NHS Foundation Trust, Southampton, UK
| | - Martijn C Nawijn
- Groningen Research Institute for Asthma and COPD (GRIAC), University Medical Center Groningen, University of Groningen, Groningen, the Netherlands
- Department of Pathology and Medical Biology, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands
| | - Sara Fontanella
- National Heart and Lung Institute, Imperial College London, London, UK
- National Institute for Health and Care Research Imperial Biomedical Research Centre (BRC), London, UK
| | - Adnan Custovic
- National Heart and Lung Institute, Imperial College London, London, UK
- National Institute for Health and Care Research Imperial Biomedical Research Centre (BRC), London, UK
| | - Gerard H Koppelman
- Department of Pediatric Pulmonology and Pediatric Allergology, Beatrix Children's Hospital, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands
- Groningen Research Institute for Asthma and COPD (GRIAC), University Medical Center Groningen, University of Groningen, Groningen, the Netherlands
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14
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Ashfaq A, Gray GM, Carapelluci J, Amankwah EK, Rehman M, Puchalski M, Smith A, Quintessenza JA, Laks J, Ahumada LM, Asante-Korang A. Survival analysis for pediatric heart transplant patients using a novel machine learning algorithm: A UNOS analysis. J Heart Lung Transplant 2023; 42:1341-1348. [PMID: 37327979 DOI: 10.1016/j.healun.2023.06.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2023] [Revised: 05/22/2023] [Accepted: 06/09/2023] [Indexed: 06/18/2023] Open
Abstract
BACKGROUND Impact of pretransplantation risk factors on mortality in the first year after heart transplantation remains largely unknown. Using machine learning algorithms, we selected clinically relevant identifiers that could predict 1-year mortality after pediatric heart transplantation. METHODS Data were obtained from the United Network for Organ Sharing Database for years 2010-2020 for patients 0-17 years receiving their first heart transplant (N = 4150). Features were selected using subject experts and literature review. Scikit-Learn, Scikit-Survival, and Tensorflow were used. A train:test split of 70:30 was used. N-repeated k-fold validation was performed (N = 5, k = 5). Seven models were tested, Hyperparameter tuning performed using Bayesian optimization and the concordance index (C-index) was used for model assessment. RESULTS A C-index above 0.6 for test data was considered acceptable for survival analysis models. C-indices obtained were 0.60 (Cox proportional hazards), 0.61 (Cox with elastic net), 0.64 (gradient boosting), 0.64 (support vector machine), 0.68 (random forest), 0.66 (component gradient boosting), and 0.54 (survival trees). Machine learning models show an improvement over the traditional Cox proportional hazards model, with random forest performing the best on the test set. Analysis of the feature importance for the gradient boosted model found that the top 5 features were the most recent serum total bilirubin, the travel distance from the transplant center, the patient body mass index, the deceased donor terminal Serum glutamic pyruvic transaminase/Alanine transaminase (SGPT/ALT), and the donor PCO2. CONCLUSIONS Combination of machine learning and expert-based methodology of selecting predictors of survival for pediatric heart transplantation provides a reasonable prediction of 1- and 3-year survival outcomes. SHapley Additive exPlanations can be an effective tool for modeling and visualizing nonlinear interactions.
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Affiliation(s)
- Awais Ashfaq
- From the Cardiovascular Surgery, Heart Institute, Johns Hopkins All Children's Hospital, St. Petersburg, Florida.
| | - Geoffrey M Gray
- Center for Pediatric Data Science and Analytic Methodology, Johns Hopkins All Children's Hospital, St. Petersburg, Florida
| | - Jennifer Carapelluci
- Heart Transplantation, Cardiomyopathy and Heart Failure, Heart Institute, Johns Hopkins All Children's Hospital, St. Petersburg, Florida
| | - Ernest K Amankwah
- Epidemiology and Biostatistics, Johns Hopkins All Children's Hospital, St. Petersburg, Florida
| | - Mohamed Rehman
- From the Cardiovascular Surgery, Heart Institute, Johns Hopkins All Children's Hospital, St. Petersburg, Florida; Department of Anesthesia and Pain Medicine, Johns Hopkins All Children's Hospital, St. Petersburg, Florida
| | - Michael Puchalski
- Division of Cardiology, Heart Institute, Johns Hopkins All Children's Hospital, St. Petersburg, Florida
| | - Andrew Smith
- and the Division of Cardiac Critical Care, Heart Institute, Johns Hopkins All Children's Hospital, St. Petersburg, Florida
| | - James A Quintessenza
- From the Cardiovascular Surgery, Heart Institute, Johns Hopkins All Children's Hospital, St. Petersburg, Florida
| | - Jessica Laks
- Heart Transplantation, Cardiomyopathy and Heart Failure, Heart Institute, Johns Hopkins All Children's Hospital, St. Petersburg, Florida
| | - Luis M Ahumada
- Center for Pediatric Data Science and Analytic Methodology, Johns Hopkins All Children's Hospital, St. Petersburg, Florida
| | - Alfred Asante-Korang
- Heart Transplantation, Cardiomyopathy and Heart Failure, Heart Institute, Johns Hopkins All Children's Hospital, St. Petersburg, Florida
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15
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Lv L, Zhang Z, Zhang D, Chen Q, Liu Y, Qiu Y, Fu W, Yin X, Chen X. Machine‐learning radiomics to predict bone marrow metastasis of neuroblastoma using magnetic resonance imaging. CANCER INNOVATION 2023; 2:405-415. [DOI: 10.1002/cai2.92] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/20/2023] [Accepted: 07/17/2023] [Indexed: 11/15/2023]
Abstract
AbstractBackgroundNeuroblastoma is one common pediatric malignancy notorious for high temporal and spatial heterogeneities. More than half of its patients develop distant metastases involving vascularized organs, especially the bone marrow. It is thus necessary to have an economical, noninvasive method without much radiation for follow‐ups. Radiomics has been used in many cancers to assist accurate diagnosis but not yet in bone marrow metastasis in neuroblastoma.MethodsA total of 182 patients with neuroblastoma were retrospectively collected and randomly divided into the training and validation sets. Five‐hundred and seventy‐two radiomics features were extracted from magnetic resonance imaging, among which 41 significant ones were selected via T‐test for model development. We attempted 13 machine‐learning algorithms and eventually chose three best‐performed models. The integrative performance evaluations are based on the area under the curves (AUCs), calibration curves, risk deciles plots, and other indexes.ResultsExtreme gradient boosting, random forest (RF), and adaptive boosting were the top three to predict bone marrow metastases in neuroblastoma while RF was the most accurate one. Its AUC was 0.90 (0.86–0.93), F1 score was 0.82, sensitivity was 0.76, and negative predictive value was 0.79 in the training set. The values were 0.82 (0.71–0.93), 0.80, 0.75, and 0.92 in the validation set, respectively.ConclusionsRadiomics models are likely to contribute more to metastatic diagnoses and the formulation of personalized healthcare strategies in clinics. It has great potential of being a revolutionary method to replace traditional interventions in the future.
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Affiliation(s)
- Lin Lv
- Department of Urology Surgery SunYat‐Sen Memorial Hospital Guangzhou Guangdong China
- Sun Yat‐Sen University of Medical School Guangzhou Guangdong China
| | - Zhengtao Zhang
- Guangzhou Women and Children's Medical Center Guangzhou Guangdong China
| | - Dongbo Zhang
- Breast Tumor Center Sun Yat‐Sen Memorial Hospital Guangzhou Guangdong China
| | - Qinchang Chen
- Guangdong Provincial People's Hospital Guangzhou Guangdong China
| | - Yuanfang Liu
- Department of Radiology Sun Yat‐Sen Memorial Hospital Guangzhou Guangdong China
| | - Ya Qiu
- Department of Radiology Sun Yat‐Sen Memorial Hospital Guangzhou Guangdong China
| | - Wen Fu
- Guangzhou Women and Children's Medical Center Guangzhou Guangdong China
| | - Xuntao Yin
- Department of Radiology Guangzhou Women and Children's Medical Center Guangzhou Guangdong China
| | - Xiong Chen
- Department of Urology Surgery SunYat‐Sen Memorial Hospital Guangzhou Guangdong China
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16
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Razavi R, Xue G, Akpan IJ. Predicting Sociodemographic Attributes from Mobile Usage Patterns: Applications and Privacy Implications. BIG DATA 2023. [PMID: 37582212 DOI: 10.1089/big.2022.0182] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/17/2023]
Abstract
When users interact with their mobile devices, they leave behind unique digital footprints that can be viewed as predictive proxies that reveal an array of users' characteristics, including their demographics. Predicting users' demographics based on mobile usage can provide significant benefits for service providers and users, including improving customer targeting, service personalization, and market research efforts. This study uses machine learning algorithms and mobile usage data from 235 demographically diverse users to examine the accuracy of predicting their sociodemographic attributes (age, gender, income, and education) from mobile usage metadata, filling the gap in the current literature by quantifying the predictive power of each attribute and discussing the practical applications and privacy implications. According to the results, gender can be most accurately predicted (balanced accuracy = 0.862) from mobile usage footprints, whereas predicting users' education level is more challenging (balanced accuracy = 0.719). Moreover, the classification models were able to classify users based on whether their age or income was above or below a certain threshold with acceptable accuracy. The study also presents the practical applications of inferring demographic attributes from mobile usage data and discusses the implications of the findings, such as privacy and discrimination risks, from the perspectives of different stakeholders.
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Affiliation(s)
- Rouzbeh Razavi
- Department of Management and Information Systems, Kent State University, Kent, Ohio, USA
| | - Guisen Xue
- Department of Management and Information Systems, Kent State University, Kent, Ohio, USA
| | - Ikpe Justice Akpan
- Department of Management and Information Systems, Kent State University at Tuscarawas, New Philadelphia, Ohio, USA
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17
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Sheta A, Thaher T, Surani SR, Turabieh H, Braik M, Too J, Abu-El-Rub N, Mafarjah M, Chantar H, Subramanian S. Diagnosis of Obstructive Sleep Apnea Using Feature Selection, Classification Methods, and Data Grouping Based Age, Sex, and Race. Diagnostics (Basel) 2023; 13:2417. [PMID: 37510161 PMCID: PMC10377846 DOI: 10.3390/diagnostics13142417] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2023] [Revised: 07/13/2023] [Accepted: 07/15/2023] [Indexed: 07/30/2023] Open
Abstract
Obstructive sleep apnea (OSA) is a prevalent sleep disorder that affects approximately 3-7% of males and 2-5% of females. In the United States alone, 50-70 million adults suffer from various sleep disorders. OSA is characterized by recurrent episodes of breathing cessation during sleep, thereby leading to adverse effects such as daytime sleepiness, cognitive impairment, and reduced concentration. It also contributes to an increased risk of cardiovascular conditions and adversely impacts patient overall quality of life. As a result, numerous researchers have focused on developing automated detection models to identify OSA and address these limitations effectively and accurately. This study explored the potential benefits of utilizing machine learning methods based on demographic information for diagnosing the OSA syndrome. We gathered a comprehensive dataset from the Torr Sleep Center in Corpus Christi, Texas, USA. The dataset comprises 31 features, including demographic characteristics such as race, age, sex, BMI, Epworth score, M. Friedman tongue position, snoring, and more. We devised a novel process encompassing pre-processing, data grouping, feature selection, and machine learning classification methods to achieve the research objectives. The classification methods employed in this study encompass decision tree (DT), naive Bayes (NB), k-nearest neighbor (kNN), support vector machine (SVM), linear discriminant analysis (LDA), logistic regression (LR), and subspace discriminant (Ensemble) classifiers. Through rigorous experimentation, the results indicated the superior performance of the optimized kNN and SVM classifiers for accurately classifying sleep apnea. Moreover, significant enhancements in model accuracy were observed when utilizing the selected demographic variables and employing data grouping techniques. For instance, the accuracy percentage demonstrated an approximate improvement of 4.5%, 5%, and 10% with the feature selection approach when applied to the grouped data of Caucasians, females, and individuals aged 50 or below, respectively. Furthermore, a comparison with prior studies confirmed that effective data grouping and proper feature selection yielded superior performance in OSA detection when combined with an appropriate classification method. Overall, the findings of this research highlight the importance of leveraging demographic information, employing proper feature selection techniques, and utilizing optimized classification models for accurate and efficient OSA diagnosis.
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Affiliation(s)
- Alaa Sheta
- Computer Science Department, Southern Connecticut State University, New Haven, CT 06514, USA
| | - Thaer Thaher
- Department of Computer Systems Engineering, Arab American University, Jenin P.O. Box 240, Palestine
| | - Salim R Surani
- Department of Pulmonary, Critical Care & Sleep Medicine, Texas A&M University, College Station, TX 77843, USA
| | - Hamza Turabieh
- Health Management and Informatics Department, School of Medicine, University of Missouri, Columbia, MO 65212, USA
| | - Malik Braik
- Department of Computer Science, Al-Balqa Applied University, Salt 19117, Jordan
| | - Jingwei Too
- Faculty of Electrical Engineering, Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, Durian Tunggal 76100, Melaka, Malaysia
| | - Noor Abu-El-Rub
- Center of Medical Informatics and Enterprise Analytics, University of Kansas Medical Center, Kansas City, KS 66160, USA
| | - Majdi Mafarjah
- Department of Computer Science, Birzeit University, Birzeit P.O. Box 14, Palestine
| | - Hamouda Chantar
- Faculty of Information Technology, Sebha University, Sebha 18758, Libya
| | - Shyam Subramanian
- Pulmonary, Critical Care & Sleep Medicine, Sutter Health, Tracy, CA 95376, USA
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18
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Chigaev M, Smith JS, Anaya S, Nebgen B, Bettencourt M, Barros K, Lubbers N. Lightweight and effective tensor sensitivity for atomistic neural networks. J Chem Phys 2023; 158:2889493. [PMID: 37158328 DOI: 10.1063/5.0142127] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2023] [Accepted: 04/20/2023] [Indexed: 05/10/2023] Open
Abstract
Atomistic machine learning focuses on the creation of models that obey fundamental symmetries of atomistic configurations, such as permutation, translation, and rotation invariances. In many of these schemes, translation and rotation invariance are achieved by building on scalar invariants, e.g., distances between atom pairs. There is growing interest in molecular representations that work internally with higher rank rotational tensors, e.g., vector displacements between atoms, and tensor products thereof. Here, we present a framework for extending the Hierarchically Interacting Particle Neural Network (HIP-NN) with Tensor Sensitivity information (HIP-NN-TS) from each local atomic environment. Crucially, the method employs a weight tying strategy that allows direct incorporation of many-body information while adding very few model parameters. We show that HIP-NN-TS is more accurate than HIP-NN, with negligible increase in parameter count, for several datasets and network sizes. As the dataset becomes more complex, tensor sensitivities provide greater improvements to model accuracy. In particular, HIP-NN-TS achieves a record mean absolute error of 0.927 kcalmol for conformational energy variation on the challenging COMP6 benchmark, which includes a broad set of organic molecules. We also compare the computational performance of HIP-NN-TS to HIP-NN and other models in the literature.
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Affiliation(s)
- Michael Chigaev
- Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USA
- Center for Nonlinear Studies, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USA
| | - Justin S Smith
- Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USA
- Center for Nonlinear Studies, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USA
- NVIDIA, 2788 San Tomas Expy, Santa Clara, California 95051, USA
| | - Steven Anaya
- High Performance Computing Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USA
- Computer, Computational, and Statistical Sciences Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USA
| | - Benjamin Nebgen
- Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USA
| | | | - Kipton Barros
- Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USA
- Center for Nonlinear Studies, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USA
| | - Nicholas Lubbers
- Computer, Computational, and Statistical Sciences Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USA
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19
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Magris M, Iosifidis A. Bayesian learning for neural networks: an algorithmic survey. Artif Intell Rev 2023. [DOI: 10.1007/s10462-023-10443-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/17/2023]
Abstract
AbstractThe last decade witnessed a growing interest in Bayesian learning. Yet, the technicality of the topic and the multitude of ingredients involved therein, besides the complexity of turning theory into practical implementations, limit the use of the Bayesian learning paradigm, preventing its widespread adoption across different fields and applications. This self-contained survey engages and introduces readers to the principles and algorithms of Bayesian Learning for Neural Networks. It provides an introduction to the topic from an accessible, practical-algorithmic perspective. Upon providing a general introduction to Bayesian Neural Networks, we discuss and present both standard and recent approaches for Bayesian inference, with an emphasis on solutions relying on Variational Inference and the use of Natural gradients. We also discuss the use of manifold optimization as a state-of-the-art approach to Bayesian learning. We examine the characteristic properties of all the discussed methods, and provide pseudo-codes for their implementation, paying attention to practical aspects, such as the computation of the gradients.
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20
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Khare SK, Bajaj V, Acharya UR. SchizoNET: a robust and accurate Margenau-Hill time-frequency distribution based deep neural network model for schizophrenia detection using EEG signals. Physiol Meas 2023; 44. [PMID: 36787641 DOI: 10.1088/1361-6579/acbc06] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2022] [Accepted: 02/14/2023] [Indexed: 02/16/2023]
Abstract
Objective.Schizophrenia (SZ) is a severe chronic illness characterized by delusions, cognitive dysfunctions, and hallucinations that impact feelings, behaviour, and thinking. Timely detection and treatment of SZ are necessary to avoid long-term consequences. Electroencephalogram (EEG) signals are one form of a biomarker that can reveal hidden changes in the brain during SZ. However, the EEG signals are non-stationary in nature with low amplitude. Therefore, extracting the hidden information from the EEG signals is challenging.Approach.The time-frequency domain is crucial for the automatic detection of SZ. Therefore, this paper presents the SchizoNET model combining the Margenau-Hill time-frequency distribution (MH-TFD) and convolutional neural network (CNN). The instantaneous information of EEG signals is captured in the time-frequency domain using MH-TFD. The time-frequency amplitude is converted to two-dimensional plots and fed to the developed CNN model.Results.The SchizoNET model is developed using three different validation techniques, including holdout, five-fold cross-validation, and ten-fold cross-validation techniques using three separate public SZ datasets (Dataset 1, 2, and 3). The proposed model achieved an accuracy of 97.4%, 99.74%, and 96.35% on Dataset 1 (adolescents: 45 SZ and 39 HC subjects), Dataset 2 (adults: 14 SZ and 14 HC subjects), and Dataset 3 (adults: 49 SZ and 32 HC subjects), respectively. We have also evaluated six performance parameters and the area under the curve to evaluate the performance of our developed model.Significance.The SchizoNET is robust, effective, and accurate, as it performed better than the state-of-the-art techniques. To the best of our knowledge, this is the first work to explore three publicly available EEG datasets for the automated detection of SZ. Our SchizoNET model can help neurologists detect the SZ in various scenarios.
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Affiliation(s)
- Smith K Khare
- Electrical and Computer Engineering Department, Aarhus University, Denmark
| | - Varun Bajaj
- Discipline of Electronics and Communication Engineering, Indian Institute of Information Technology, Design, and Manufacturing (IIITDM) Jabalpur, India
| | - U Rajendra Acharya
- School of Mathematics, Physics, and Computing, University of Southern Queensland, Springfield, Australia.,Department of Biomedical Engineering, School of Science and Technology, University of Social Sciences, Singapore.,Department of Biomedical Informatics and Medical Engineering, Asia University, Taiwan.,Distinguished Professor, Kumamoto University, Japan.,Adjunct Professor, University of Malaya, Malaysia
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21
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Parker VL, Winter MC, Tidy JA, Hancock BW, Palmer JE, Sarwar N, Kaur B, McDonald K, Aguiar X, Singh K, Unsworth N, Jabbar I, Pacey AA, Harrison RF, Seckl MJ. PREDICT-GTN 1: Can we improve the FIGO scoring system in gestational trophoblastic neoplasia? Int J Cancer 2023; 152:986-997. [PMID: 36346113 PMCID: PMC10108153 DOI: 10.1002/ijc.34352] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2022] [Revised: 10/13/2022] [Accepted: 10/17/2022] [Indexed: 11/10/2022]
Abstract
Gestational trophoblastic neoplasia (GTN) patients are treated according to the eight-variable International Federation of Gynaecology and Obstetrics (FIGO) scoring system, that aims to predict first-line single-agent chemotherapy resistance. FIGO is imperfect with one-third of low-risk patients developing disease resistance to first-line single-agent chemotherapy. We aimed to generate simplified models that improve upon FIGO. Logistic regression (LR) and multilayer perceptron (MLP) modelling (n = 4191) generated six models (M1-6). M1, all eight FIGO variables (scored data); M2, all eight FIGO variables (scored and raw data); M3, nonimaging variables (scored data); M4, nonimaging variables (scored and raw data); M5, imaging variables (scored data); and M6, pretreatment hCG (raw data) + imaging variables (scored data). Performance was compared to FIGO using true and false positive rates, positive and negative predictive values, diagnostic odds ratio, receiver operating characteristic (ROC) curves, Bland-Altman calibration plots, decision curve analysis and contingency tables. M1-6 were calibrated and outperformed FIGO on true positive rate and positive predictive value. Using LR and MLP, M1, M2 and M4 generated small improvements to the ROC curve and decision curve analysis. M3, M5 and M6 matched FIGO or performed less well. Compared to FIGO, most (excluding LR M4 and MLP M5) had significant discordance in patient classification (McNemar's test P < .05); 55-112 undertreated, 46-206 overtreated. Statistical modelling yielded only small gains over FIGO performance, arising through recategorisation of treatment-resistant patients, with a significant proportion of under/overtreatment as the available data have been used a priori to allocate primary chemotherapy. Streamlining FIGO should now be the focus.
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Affiliation(s)
- Victoria L Parker
- Department of Oncology and Metabolism, The Medical School, The University of Sheffield, Sheffield, UK
| | - Matthew C Winter
- Department of Oncology and Metabolism, The Medical School, The University of Sheffield, Sheffield, UK.,Sheffield Centre for Trophoblastic Disease, Weston Park Cancer Centre, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, UK
| | - John A Tidy
- Sheffield Centre for Trophoblastic Disease, Weston Park Cancer Centre, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, UK
| | - Barry W Hancock
- Department of Oncology and Metabolism, The Medical School, The University of Sheffield, Sheffield, UK
| | - Julia E Palmer
- Sheffield Centre for Trophoblastic Disease, Weston Park Cancer Centre, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, UK
| | - Naveed Sarwar
- Gestational Trophoblastic Disease Centre, Department of Medical Oncology, Charing Cross Hospital, Imperial College Healthcare NHS Trust, London, UK
| | - Baljeet Kaur
- Gestational Trophoblastic Disease Centre, Department of Medical Oncology, Charing Cross Hospital, Imperial College Healthcare NHS Trust, London, UK
| | - Katie McDonald
- Sheffield Centre for Trophoblastic Disease, Weston Park Cancer Centre, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, UK
| | - Xianne Aguiar
- Gestational Trophoblastic Disease Centre, Department of Medical Oncology, Charing Cross Hospital, Imperial College Healthcare NHS Trust, London, UK
| | - Kamaljit Singh
- Sheffield Centre for Trophoblastic Disease, Weston Park Cancer Centre, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, UK
| | - Nick Unsworth
- Gestational Trophoblastic Disease Centre, Department of Medical Oncology, Charing Cross Hospital, Imperial College Healthcare NHS Trust, London, UK
| | - Imran Jabbar
- Sheffield Centre for Trophoblastic Disease, Weston Park Cancer Centre, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, UK
| | - Allan A Pacey
- Department of Oncology and Metabolism, The Medical School, The University of Sheffield, Sheffield, UK
| | - Robert F Harrison
- Department of Automatic Control and Systems Engineering, The University of Sheffield, Sheffield, UK
| | - Michael J Seckl
- Gestational Trophoblastic Disease Centre, Department of Medical Oncology, Charing Cross Hospital, Imperial College Healthcare NHS Trust, London, UK
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22
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Kulyukin VA, Coster D, Tkachenko A, Hornberger D, Kulyukin AV. Ambient Electromagnetic Radiation as a Predictor of Honey Bee ( Apis mellifera) Traffic in Linear and Non-Linear Regression: Numerical Stability, Physical Time and Energy Efficiency. SENSORS (BASEL, SWITZERLAND) 2023; 23:2584. [PMID: 36904786 PMCID: PMC10007012 DOI: 10.3390/s23052584] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/16/2023] [Revised: 02/11/2023] [Accepted: 02/21/2023] [Indexed: 06/18/2023]
Abstract
Since bee traffic is a contributing factor to hive health and electromagnetic radiation has a growing presence in the urban milieu, we investigate ambient electromagnetic radiation as a predictor of bee traffic in the hive's vicinity in an urban environment. To that end, we built two multi-sensor stations and deployed them for four and a half months at a private apiary in Logan, UT, USA. to record ambient weather and electromagnetic radiation. We placed two non-invasive video loggers on two hives at the apiary to extract omnidirectional bee motion counts from videos. The time-aligned datasets were used to evaluate 200 linear and 3,703,200 non-linear (random forest and support vector machine) regressors to predict bee motion counts from time, weather, and electromagnetic radiation. In all regressors, electromagnetic radiation was as good a predictor of traffic as weather. Both weather and electromagnetic radiation were better predictors than time. On the 13,412 time-aligned weather, electromagnetic radiation, and bee traffic records, random forest regressors had higher maximum R2 scores and resulted in more energy efficient parameterized grid searches. Both types of regressors were numerically stable.
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Affiliation(s)
| | - Daniel Coster
- Department of Mathematics and Statistics, Utah State University, Logan, UT 84322, USA
| | | | - Daniel Hornberger
- Department of Computer Science, Utah State University, Logan, UT 84322, USA
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23
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Ammar A, Koubaa A, Boulila W, Benjdira B, Alhabashi Y. A Multi-Stage Deep-Learning-Based Vehicle and License Plate Recognition System with Real-Time Edge Inference. SENSORS (BASEL, SWITZERLAND) 2023; 23:2120. [PMID: 36850714 PMCID: PMC9966104 DOI: 10.3390/s23042120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Revised: 02/02/2023] [Accepted: 02/08/2023] [Indexed: 06/18/2023]
Abstract
Video streaming-based real-time vehicle identification and license plate recognition systems are challenging to design and deploy in terms of real-time processing on edge, dealing with low image resolution, high noise, and identification. This paper addresses these issues by introducing a novel multi-stage, real-time, deep learning-based vehicle identification and license plate recognition system. The system is based on a set of algorithms that efficiently integrate two object detectors, an image classifier, and a multi-object tracker to recognize car models and license plates. The information redundancy of Saudi license plates' Arabic and English characters is leveraged to boost the license plate recognition accuracy while satisfying real-time inference performance. The system optimally achieves real-time performance on edge GPU devices and maximizes models' accuracy by taking advantage of the temporally redundant information of the video stream's frames. The edge device sends a notification of the detected vehicle and its license plate only once to the cloud after completing the processing. The system was experimentally evaluated on vehicles and license plates in real-world unconstrained environments at several parking entrance gates. It achieves 17.1 FPS on a Jetson Xavier AGX edge device with no delay. The comparison between the accuracy on the videos and on static images extracted from them shows that the processing of video streams using this proposed system enhances the relative accuracy of the car model and license plate recognition by 13% and 40%, respectively. This research work has won two awards in 2021 and 2022.
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Hagendorff T, Fabi S. Why we need biased AI: How including cognitive biases can enhance AI systems. J EXP THEOR ARTIF IN 2023. [DOI: 10.1080/0952813x.2023.2178517] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/16/2023]
Affiliation(s)
- Thilo Hagendorff
- Cluster of Excellence 'Machine Learning – New Perspectives for Science', University of Tuebingen, Tuebingen, Germany
| | - Sarah Fabi
- Department of Cognitive Science, University of California San Diego, San Diego, USA
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Allaart LJH, Spanning SV, Lafosse L, Lafosse T, Ladermann A, Athwal GS, Hendrickx LAM, Doornberg JN, van den Bekerom MPJ, Buijze GA. Developing a machine learning algorithm to predict probability of retear and functional outcomes in patients undergoing rotator cuff repair surgery: protocol for a retrospective, multicentre study. BMJ Open 2023; 13:e063673. [PMID: 36764713 PMCID: PMC9923257 DOI: 10.1136/bmjopen-2022-063673] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/12/2023] Open
Abstract
INTRODUCTION The effectiveness of rotator cuff tear repair surgery is influenced by multiple patient-related, pathology-centred and technical factors, which is thought to contribute to the reported retear rates between 17% and 94%. Adequate patient selection is thought to be essential in reaching satisfactory results. However, no clear consensus has been reached on which factors are most predictive of successful surgery. A clinical decision tool that encompassed all aspects is still to be made. Artificial intelligence (AI) and machine learning algorithms use complex self-learning models that can be used to make patient-specific decision-making tools. The aim of this study is to develop and train an algorithm that can be used as an online available clinical prediction tool, to predict the risk of retear in patients undergoing rotator cuff repair. METHODS AND ANALYSIS This is a retrospective, multicentre, cohort study using pooled individual patient data from multiple studies of patients who have undergone rotator cuff repair and were evaluated by advanced imaging for healing at a minimum of 6 months after surgery. This study consists of two parts. Part one: collecting all potential factors that might influence retear risks from retrospective multicentre data, aiming to include more than 1000 patients worldwide. Part two: combining all influencing factors into a model that can clinically be used as a prediction tool using machine learning. ETHICS AND DISSEMINATION For safe multicentre data exchange and analysis, our Machine Learning Consortium adheres to the WHO regulation 'Policy on Use and Sharing of Data Collected by WHO in Member States Outside the Context of Public Health Emergencies'. The study results will be disseminated through publication in a peer-reviewed journal. Institutional Review Board approval does not apply to the current study protocol.
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Affiliation(s)
- Laurens J H Allaart
- Orthopaedic Surgery, Clinique Générale Annecy, Annecy, Auvergne-Rhône-Alpes, France
- Department of Human Movement Sciences, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Sanne van Spanning
- Department of Human Movement Sciences, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Shoulder and Elbow Unit, Joint Research, Department of Orthopaedic Surgery, OLVG, Amsterdam, The Netherlands
| | - Laurent Lafosse
- Orthopaedic Surgery, Clinique Générale Annecy, Annecy, Auvergne-Rhône-Alpes, France
| | - Thibault Lafosse
- Orthopaedic Surgery, Clinique Générale Annecy, Annecy, Auvergne-Rhône-Alpes, France
| | - Alexandre Ladermann
- Division of Orthopaedics and Trauma Surgery, La Tour Hopital Prive SA, Meyrin, Switzerland
- Faculty of Medicine, University of Geneva, Geneve, Switzerland
| | - George S Athwal
- Roth McFarlane Hand and Upper Limb Center, Schulich School of Medicine and Dentistry, London, Ontario, Canada
| | - Laurent A M Hendrickx
- Department of Orthopedic Surgery, University of Amsterdam, Amsterdam, The Netherlands
- Orthopaedic and Trauma Surgery, Flinders University, Adelaide, South Australia, Australia
| | - Job N Doornberg
- Orthopaedic and Trauma Surgery, Flinders University, Adelaide, South Australia, Australia
- Orthopaedic Surgery, University Medical Centre Groningen, Groningen, The Netherlands
| | | | - Geert Alexander Buijze
- Orthopaedic Surgery, Clinique Générale Annecy, Annecy, Auvergne-Rhône-Alpes, France
- Department of Orthopedic Surgery, University of Amsterdam, Amsterdam, The Netherlands
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Trenkwalder T, Lachmann M, Stolz L, Fortmeier V, Covarrubias HAA, Rippen E, Schürmann F, Presch A, von Scheidt M, Ruff C, Hesse A, Gerçek M, Mayr NP, Ott I, Schuster T, Harmsen G, Yuasa S, Kufner S, Hoppmann P, Kupatt C, Schunkert H, Kastrati A, Laugwitz KL, Rudolph V, Joner M, Hausleiter J, Xhepa E. Machine learning identifies pathophysiologically and prognostically informative phenotypes among patients with mitral regurgitation undergoing transcatheter edge-to-edge repair. Eur Heart J Cardiovasc Imaging 2023; 24:574-587. [PMID: 36735333 DOI: 10.1093/ehjci/jead013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/25/2022] [Accepted: 01/06/2023] [Indexed: 02/04/2023] Open
Abstract
AIMS Patients with mitral regurgitation (MR) present with considerable heterogeneity in cardiac damage depending on underlying aetiology, disease progression, and comorbidities. This study aims to capture their cardiopulmonary complexity by employing a machine-learning (ML)-based phenotyping approach. METHODS AND RESULTS Data were obtained from 1426 patients undergoing mitral valve transcatheter edge-to-edge repair (MV TEER) for MR. The ML model was developed using 609 patients (derivation cohort) and validated on 817 patients from two external institutions. Phenotyping was based on echocardiographic data, and ML-derived phenotypes were correlated with 5-year outcomes. Unsupervised agglomerative clustering revealed four phenotypes among the derivation cohort: Cluster 1 showed preserved left ventricular ejection fraction (LVEF; 56.5 ± 7.79%) and regular left ventricular end-systolic diameter (LVESD; 35.2 ± 7.52 mm); 5-year survival in Cluster 1, hereinafter serving as a reference, was 60.9%. Cluster 2 presented with preserved LVEF (55.7 ± 7.82%) but showed the largest mitral valve effective regurgitant orifice area (0.623 ± 0.360 cm2) and highest systolic pulmonary artery pressures (68.4 ± 16.2 mmHg); 5-year survival ranged at 43.7% (P-value: 0.032). Cluster 3 was characterized by impaired LVEF (31.0 ± 10.4%) and enlarged LVESD (53.2 ± 10.9 mm); 5-year survival was reduced to 38.3% (P-value: <0.001). The poorest 5-year survival (23.8%; P-value: <0.001) was observed in Cluster 4 with biatrial dilatation (left atrial volume: 312 ± 113 mL; right atrial area: 46.0 ± 8.83 cm2) although LVEF was only slightly reduced (51.5 ± 11.0%). Importantly, the prognostic significance of ML-derived phenotypes was externally confirmed. CONCLUSION ML-enabled phenotyping captures the complexity of extra-mitral valve cardiac damage, which does not necessarily occur in a sequential fashion. This novel phenotyping approach can refine risk stratification in patients undergoing MV TEER in the future.
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Affiliation(s)
- Teresa Trenkwalder
- Department of Cardiology, German Heart Center Munich, Technical University of Munich, Lazarettstrasse 36, 80636 Munich, Germany
- DZHK (German Center for Cardiovascular Research), Partner Site Munich Heart Alliance, Pettenkoferstrasse 8a & 9, 80336 Munich, Germany
| | - Mark Lachmann
- DZHK (German Center for Cardiovascular Research), Partner Site Munich Heart Alliance, Pettenkoferstrasse 8a & 9, 80336 Munich, Germany
- First Department of Medicine, Klinikum rechts der Isar, Technical University of Munich, Ismaninger Strasse 22, 81675 Munich, Germany
| | - Lukas Stolz
- Medizinische Klinik und Poliklinik I, Klinikum der Universität München, Ludwig Maximilians University of Munich, Marchioninistrasse 15, 81377 Munich, Germany
| | - Vera Fortmeier
- Department of General and Interventional Cardiology, Heart and Diabetes Center Northrhine-Westfalia, Ruhr University Bochum, Georgstrasse 11, 32545 Bad Oeynhausen, Germany
| | | | - Elena Rippen
- DZHK (German Center for Cardiovascular Research), Partner Site Munich Heart Alliance, Pettenkoferstrasse 8a & 9, 80336 Munich, Germany
- First Department of Medicine, Klinikum rechts der Isar, Technical University of Munich, Ismaninger Strasse 22, 81675 Munich, Germany
| | - Friederike Schürmann
- Department of Cardiology, German Heart Center Munich, Technical University of Munich, Lazarettstrasse 36, 80636 Munich, Germany
| | - Antonia Presch
- Department of Cardiology, German Heart Center Munich, Technical University of Munich, Lazarettstrasse 36, 80636 Munich, Germany
| | - Moritz von Scheidt
- Department of Cardiology, German Heart Center Munich, Technical University of Munich, Lazarettstrasse 36, 80636 Munich, Germany
- DZHK (German Center for Cardiovascular Research), Partner Site Munich Heart Alliance, Pettenkoferstrasse 8a & 9, 80336 Munich, Germany
| | - Celine Ruff
- Department of Cardiology, German Heart Center Munich, Technical University of Munich, Lazarettstrasse 36, 80636 Munich, Germany
| | - Amelie Hesse
- DZHK (German Center for Cardiovascular Research), Partner Site Munich Heart Alliance, Pettenkoferstrasse 8a & 9, 80336 Munich, Germany
- First Department of Medicine, Klinikum rechts der Isar, Technical University of Munich, Ismaninger Strasse 22, 81675 Munich, Germany
| | - Muhammed Gerçek
- Department of General and Interventional Cardiology, Heart and Diabetes Center Northrhine-Westfalia, Ruhr University Bochum, Georgstrasse 11, 32545 Bad Oeynhausen, Germany
| | - N Patrick Mayr
- Institute of Anesthesiology, German Heart Center Munich, Technical University of Munich, Lazarettstrasse 36, 80636 Munich, Germany
| | - Ilka Ott
- Department of Cardiology, Helios Klinikum Pforzheim, Kanzlerstrasse 2-6, 75175 Pforzheim, Germany
| | - Tibor Schuster
- Department of Family Medicine, McGill University, 5858 Chemin de la Côte-des-Neiges, Montréal, QC, Canada
| | - Gerhard Harmsen
- Department of Physics, University of Johannesburg, Auckland Park, 5 Kingsway Avenue, Rossmore, 2092 Johannesburg, South Africa
| | - Shinsuke Yuasa
- Department of Cardiology, Keio University School of Medicine, 35-Shinanomachi, Shinjuku-ku, 160-8582 Tokyo, Japan
| | - Sebastian Kufner
- Department of Cardiology, German Heart Center Munich, Technical University of Munich, Lazarettstrasse 36, 80636 Munich, Germany
- DZHK (German Center for Cardiovascular Research), Partner Site Munich Heart Alliance, Pettenkoferstrasse 8a & 9, 80336 Munich, Germany
| | - Petra Hoppmann
- DZHK (German Center for Cardiovascular Research), Partner Site Munich Heart Alliance, Pettenkoferstrasse 8a & 9, 80336 Munich, Germany
- First Department of Medicine, Klinikum rechts der Isar, Technical University of Munich, Ismaninger Strasse 22, 81675 Munich, Germany
| | - Christian Kupatt
- DZHK (German Center for Cardiovascular Research), Partner Site Munich Heart Alliance, Pettenkoferstrasse 8a & 9, 80336 Munich, Germany
- First Department of Medicine, Klinikum rechts der Isar, Technical University of Munich, Ismaninger Strasse 22, 81675 Munich, Germany
| | - Heribert Schunkert
- Department of Cardiology, German Heart Center Munich, Technical University of Munich, Lazarettstrasse 36, 80636 Munich, Germany
- DZHK (German Center for Cardiovascular Research), Partner Site Munich Heart Alliance, Pettenkoferstrasse 8a & 9, 80336 Munich, Germany
| | - Adnan Kastrati
- Department of Cardiology, German Heart Center Munich, Technical University of Munich, Lazarettstrasse 36, 80636 Munich, Germany
- DZHK (German Center for Cardiovascular Research), Partner Site Munich Heart Alliance, Pettenkoferstrasse 8a & 9, 80336 Munich, Germany
| | - Karl-Ludwig Laugwitz
- DZHK (German Center for Cardiovascular Research), Partner Site Munich Heart Alliance, Pettenkoferstrasse 8a & 9, 80336 Munich, Germany
- First Department of Medicine, Klinikum rechts der Isar, Technical University of Munich, Ismaninger Strasse 22, 81675 Munich, Germany
| | - Volker Rudolph
- Department of General and Interventional Cardiology, Heart and Diabetes Center Northrhine-Westfalia, Ruhr University Bochum, Georgstrasse 11, 32545 Bad Oeynhausen, Germany
| | - Michael Joner
- Department of Cardiology, German Heart Center Munich, Technical University of Munich, Lazarettstrasse 36, 80636 Munich, Germany
- DZHK (German Center for Cardiovascular Research), Partner Site Munich Heart Alliance, Pettenkoferstrasse 8a & 9, 80336 Munich, Germany
| | - Jörg Hausleiter
- DZHK (German Center for Cardiovascular Research), Partner Site Munich Heart Alliance, Pettenkoferstrasse 8a & 9, 80336 Munich, Germany
- Medizinische Klinik und Poliklinik I, Klinikum der Universität München, Ludwig Maximilians University of Munich, Marchioninistrasse 15, 81377 Munich, Germany
| | - Erion Xhepa
- Department of Cardiology, German Heart Center Munich, Technical University of Munich, Lazarettstrasse 36, 80636 Munich, Germany
- DZHK (German Center for Cardiovascular Research), Partner Site Munich Heart Alliance, Pettenkoferstrasse 8a & 9, 80336 Munich, Germany
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Query-adaptive training data recommendation for cross-building predictive modeling. Knowl Inf Syst 2023. [DOI: 10.1007/s10115-022-01771-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
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Ye Z, An S, Gao Y, Xie E, Zhao X, Guo Z, Li Y, Shen N, Ren J, Zheng J. The prediction of in-hospital mortality in chronic kidney disease patients with coronary artery disease using machine learning models. Eur J Med Res 2023; 28:33. [PMID: 36653875 PMCID: PMC9847092 DOI: 10.1186/s40001-023-00995-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2022] [Accepted: 01/04/2023] [Indexed: 01/19/2023] Open
Abstract
OBJECTIVE Chronic kidney disease (CKD) patients with coronary artery disease (CAD) in the intensive care unit (ICU) have higher in-hospital mortality and poorer prognosis than patients with either single condition. The objective of this study is to develop a novel model that can predict the in-hospital mortality of that kind of patient in the ICU using machine learning methods. METHODS Data of CKD patients with CAD were extracted from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database. Boruta algorithm was conducted for the feature selection process. Eight machine learning algorithms, such as logistic regression (LR), random forest (RF), Decision Tree, K-nearest neighbors (KNN), Gradient Boosting Decision Tree Machine (GBDT), Support Vector Machine (SVM), Neural Network (NN), and Extreme Gradient Boosting (XGBoost), were conducted to construct the predictive model for in-hospital mortality and performance was evaluated by average precision (AP) and area under the receiver operating characteristic curve (AUC). Shapley Additive Explanations (SHAP) algorithm was applied to explain the model visually. Moreover, data from the Telehealth Intensive Care Unit Collaborative Research Database (eICU-CRD) were acquired as an external validation set. RESULTS 3590 and 1657 CKD patients with CAD were acquired from MIMIC-IV and eICU-CRD databases, respectively. A total of 78 variables were selected for the machine learning model development process. Comparatively, GBDT had the highest predictive performance according to the results of AUC (0.946) and AP (0.778). The SHAP method reveals the top 20 factors based on the importance ranking. In addition, GBDT had good predictive value and a certain degree of clinical value in the external validation according to the AUC (0.865), AP (0.672), decision curve analysis, and calibration curve. CONCLUSION Machine learning algorithms, especially GBDT, can be reliable tools for accurately predicting the in-hospital mortality risk for CKD patients with CAD in the ICU. This contributed to providing optimal resource allocation and reducing in-hospital mortality by tailoring precise management and implementation of early interventions.
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Affiliation(s)
- Zixiang Ye
- grid.11135.370000 0001 2256 9319Department of Cardiology, Peking University China-Japan Friendship School of Clinical Medicine, Beijing, 100029 China
| | - Shuoyan An
- grid.415954.80000 0004 1771 3349Department of Cardiology, China-Japan Friendship Hospital, 2 Yinghua Dongjie, Chaoyang District, Beijing, 100029 China
| | - Yanxiang Gao
- grid.415954.80000 0004 1771 3349Department of Cardiology, China-Japan Friendship Hospital, 2 Yinghua Dongjie, Chaoyang District, Beijing, 100029 China
| | - Enmin Xie
- grid.506261.60000 0001 0706 7839Graduate School of Peking Union Medical College, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100029 China
| | - Xuecheng Zhao
- grid.415954.80000 0004 1771 3349Department of Cardiology, China-Japan Friendship Hospital, 2 Yinghua Dongjie, Chaoyang District, Beijing, 100029 China
| | - Ziyu Guo
- grid.11135.370000 0001 2256 9319Department of Cardiology, Peking University China-Japan Friendship School of Clinical Medicine, Beijing, 100029 China
| | - Yike Li
- grid.506261.60000 0001 0706 7839Graduate School of Peking Union Medical College, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100029 China
| | - Nan Shen
- grid.11135.370000 0001 2256 9319Department of Cardiology, Peking University China-Japan Friendship School of Clinical Medicine, Beijing, 100029 China
| | - Jingyi Ren
- grid.415954.80000 0004 1771 3349Department of Cardiology, China-Japan Friendship Hospital, 2 Yinghua Dongjie, Chaoyang District, Beijing, 100029 China
| | - Jingang Zheng
- grid.11135.370000 0001 2256 9319Department of Cardiology, Peking University China-Japan Friendship School of Clinical Medicine, Beijing, 100029 China ,grid.415954.80000 0004 1771 3349Department of Cardiology, China-Japan Friendship Hospital, 2 Yinghua Dongjie, Chaoyang District, Beijing, 100029 China
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On shadowed set approximation methods. Soft comput 2023. [DOI: 10.1007/s00500-023-07821-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
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30
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De Lara MLD. Persistent homology classification algorithm. PeerJ Comput Sci 2023; 9:e1195. [PMID: 37346603 PMCID: PMC10280283 DOI: 10.7717/peerj-cs.1195] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2021] [Accepted: 12/01/2022] [Indexed: 06/23/2023]
Abstract
Data classification is an important aspect of machine learning, as it is utilized to solve issues in a wide variety of contexts. There are numerous classifiers, but there is no single best-performing classifier for all types of data, as the no free lunch theorem implies. Topological data analysis is an emerging topic concerned with the shape of data. One of the key tools in this field for analyzing the shape or topological properties of a dataset is persistent homology, an algebraic topology-based method for estimating the topological features of a space of points that persists across several resolutions. This study proposes a supervised learning classification algorithm that makes use of persistent homology between training data classes in the form of persistence diagrams to predict the output category of new observations. Validation of the developed algorithm was performed on real-world and synthetic datasets. The performance of the proposed classification algorithm on these datasets was compared to that of the most widely used classifiers. Validation runs demonstrated that the proposed persistent homology classification algorithm performed at par if not better than the majority of classifiers considered.
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Affiliation(s)
- Mark Lexter D. De Lara
- Institute of Mathematical Sciences and Physics, College of Arts and Sciences, University of the Philippines Los Baños, College, Los Baños, Laguna, Philippines
- Institute of Mathematics, University of the Philippines Diliman, Quezon City, Metro Manila, Philippines
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Yu KR, Cojocaru CV, Ilinca F, Irissou E. Ensemble Methods for APS In-Flight Particle Temperature and Velocity Prediction Considering Torch Electrodes Ageing. JOURNAL OF THERMAL SPRAY TECHNOLOGY 2023; 32:175-187. [PMID: 37521320 PMCID: PMC9821370 DOI: 10.1007/s11666-022-01472-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/23/2021] [Revised: 09/09/2022] [Accepted: 09/23/2022] [Indexed: 08/01/2023]
Abstract
The nonlinear relationship between the input process parameters and in-flight particle characteristics of the atmospheric plasma spray (APS) is of paramount importance for coating properties design and quality. It is also known that the ageing of torch electrodes affects this relationship. In recent years, machine learning algorithms have proven to be able to take into account such complex nonlinear interactions. This work illustrates the application of ensemble methods to predict the in-flight particle temperature and velocity during an APS process considering torch electrodes ageing. Experiments were performed to record simultaneously the input process parameters, the in-flight powder particle characteristics and the electrodes usage time. Random Forest (RF) and Gradient Boosting (GB) were used to rank and select the features for the APS process data recorded as the electrodes aged and the corresponding predictive models were compared. The time series aspect of the multivariate APS in-flight particle characteristics data is explored. Two strategies of time series embedding are considered. The first one simply embeds the attributes and the targets from the previous n time segments considered without any modification; whereas the second strategy first performs differencing to make the time series stationary before embedding. For the present application, RF is found to be more suitable than GB since RF can predict both the in-flight particle velocity and temperature simultaneously, properly considering the interactions between the two targets. On the other hand, GB can only predict these two targets one at a time. The superior performance of both embedded predictive models and the feature rankings of them suggest that it is better to consider the APS data as time series for the in-flight particle characteristic prediction. In particular, it is demonstrated that it is advantageous to first make the time series stationary using the traditional differencing technique, even when modeling using RF.
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Affiliation(s)
- K. R. Yu
- National Research Council of Canada, AST, Boucherville, QC Canada
| | - C. V. Cojocaru
- National Research Council of Canada, AST, Boucherville, QC Canada
| | - F. Ilinca
- National Research Council of Canada, AST, Boucherville, QC Canada
| | - E. Irissou
- National Research Council of Canada, AST, Boucherville, QC Canada
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Zhang S, Han Y, Peng J, Chen Y, Zhan L, Li J. Human health risk assessment for contaminated sites: A retrospective review. ENVIRONMENT INTERNATIONAL 2023; 171:107700. [PMID: 36527872 DOI: 10.1016/j.envint.2022.107700] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/27/2022] [Revised: 12/13/2022] [Accepted: 12/14/2022] [Indexed: 06/17/2023]
Abstract
Soil contamination is a serious global hazard as contaminants can migrate to the human body through the soil, water, air, and food, threatening human health. Human Health Risk Assessment (HHRA) is a commonly used method for estimating the magnitude and probability of adverse health effects in humans that may be exposed to contaminants in contaminated environmental media in the present or future. Such estimations have improved for decades with various risk assessment frameworks and well-established models. However, the existing literature does not provide a comprehensive overview of the methods and models of HHRA that are needed to grasp the current status of HHRA and future research directions. Thus, this paper aims to systematically review the HHRA approaches and models, particularly those related to contaminated sites from peer-reviewed literature and guidelines. The approaches and models focus on methods used in hazard identification, toxicity databases in dose-response assessment, approaches and fate and transport models in exposure assessment, risk characterization, and uncertainty characterization. The features and applicability of the most commonly used HHRA tools are also described. The future research trend for HHRA for contaminated sites is also forecasted. The transition from animal experiments to new methods in risk identification, the integration and update and sharing of existing toxicity databases, the integration of human biomonitoring into the risk assessment process, and the integration of migration and transformation models and risk assessment are the way forward for risk assessment in the future. This review provides readers with an overall understanding of HHRA and a grasp of its developmental direction.
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Affiliation(s)
- Shuai Zhang
- Department of Civil Engineering, Zhejiang University, Hangzhou 310058, China; MOE Key Laboratory of Soft Soils and Geoenvironmental Engineering, Zhejiang University, Hangzhou 310058, China
| | - Yingyue Han
- Department of Civil Engineering, Zhejiang University, Hangzhou 310058, China
| | - Jingyu Peng
- Department of Civil Engineering, Zhejiang University, Hangzhou 310058, China
| | - Yunmin Chen
- Department of Civil Engineering, Zhejiang University, Hangzhou 310058, China; MOE Key Laboratory of Soft Soils and Geoenvironmental Engineering, Zhejiang University, Hangzhou 310058, China
| | - Liangtong Zhan
- Department of Civil Engineering, Zhejiang University, Hangzhou 310058, China; MOE Key Laboratory of Soft Soils and Geoenvironmental Engineering, Zhejiang University, Hangzhou 310058, China
| | - Jinlong Li
- Department of Civil Engineering, Zhejiang University, Hangzhou 310058, China; MOE Key Laboratory of Soft Soils and Geoenvironmental Engineering, Zhejiang University, Hangzhou 310058, China.
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Razavi R, Xue G. Predicting Unreported Micronutrients from Food Labels: A Machine Learning Approach (Preprint). J Med Internet Res 2022; 25:e45332. [PMID: 37043261 PMCID: PMC10134025 DOI: 10.2196/45332] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2022] [Revised: 03/11/2023] [Accepted: 03/12/2023] [Indexed: 03/13/2023] Open
Abstract
BACKGROUND Micronutrient deficiencies represent a major global health issue, with over 2 billion individuals experiencing deficiencies in essential vitamins and minerals. Food labels provide consumers with information regarding the nutritional content of food items and have been identified as a potential tool for improving diets. However, due to governmental regulations and the physical limitations of the labels, food labels often lack comprehensive information about the vitamins and minerals present in foods. As a result, information about most of the micronutrients is absent from existing food labels. OBJECTIVE This paper aims to examine the possibility of using machine learning algorithms to predict unreported micronutrients such as vitamin A (retinol), vitamin C, vitamin B1 (thiamin), vitamin B2 (riboflavin), vitamin B3 (niacin), vitamin B6, vitamin B12, vitamin E (alpha-tocopherol), vitamin K, and minerals such as magnesium, zinc, phosphorus, selenium, manganese, and copper from nutrition information provided on existing food labels. If unreported micronutrients can be predicted with acceptable accuracies from existing food labels using machine learning predictive models, such models can be integrated into mobile apps to provide consumers with additional micronutrient information about foods and help them make more informed diet decisions. METHODS Data from the Food and Nutrient Database for Dietary Studies (FNDDS) data set, representing a total of 5624 foods, were used to train a diverse set of machine learning classification and regression algorithms to predict unreported vitamins and minerals from existing food label data. For each model, hyperparameters were adjusted, and the models were evaluated using repeated cross-validation to ensure that the reported results were not subject to overfitting. RESULTS According to the results, while predicting the exact quantity of vitamins and minerals is shown to be challenging, with regression R2 varying in a wide range from 0.28 (for magnesium) to 0.92 (for manganese), the classification models can accurately predict the category ("low," "medium," or "high") level of all minerals and vitamins with accuracies exceeding 0.80. The highest classification accuracies for specific micronutrients are achieved for vitamin B12 (0.94) and phosphorus (0.94), while the lowest are for vitamin E (0.81) and selenium (0.83). CONCLUSIONS This study demonstrates the feasibility of predicting unreported micronutrients from existing food labels using machine learning algorithms. The results show that the approach has the potential to significantly improve consumer knowledge about the micronutrient content of the foods they consume. Integrating these predictive models into mobile apps can enhance their accessibility and engagement with consumers. The implications of this research for public health are noteworthy, underscoring the potential of technology to augment consumers' understanding of the micronutrient content of their diets while also facilitating the tracking of food intake and providing personalized recommendations based on the micronutrient content and individual preferences.
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Affiliation(s)
- Rouzbeh Razavi
- Department of Management and Information Systems, Kent State University, Kent, OH, United States
| | - Guisen Xue
- Department of Management and Information Systems, Kent State University, Kent, OH, United States
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Salgado Á, de Melo-Minardi RC, Giovanetti M, Veloso A, Morais-Rodrigues F, Adelino T, de Jesus R, Tosta S, Azevedo V, Lourenco J, Alcantara LCJ. Machine learning models exploring characteristic single-nucleotide signatures in yellow fever virus. PLoS One 2022; 17:e0278982. [PMID: 36508435 PMCID: PMC9744328 DOI: 10.1371/journal.pone.0278982] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2021] [Accepted: 11/29/2022] [Indexed: 12/14/2022] Open
Abstract
Yellow fever virus (YFV) is the agent of the most severe mosquito-borne disease in the tropics. Recently, Brazil suffered major YFV outbreaks with a high fatality rate affecting areas where the virus has not been reported for decades, consisting of urban areas where a large number of unvaccinated people live. We developed a machine learning framework combining three different algorithms (XGBoost, random forest and regularized logistic regression) to analyze YFV genomic sequences. This method was applied to 56 YFV sequences from human infections and 27 from non-human primate (NHPs) infections to investigate the presence of genetic signatures possibly related to disease severity (in human related sequences) and differences in PCR cycle threshold (Ct) values (in NHP related sequences). Our analyses reveal four non-synonymous single nucleotide variations (SNVs) on sequences from human infections, in proteins NS3 (E614D), NS4a (I69V), NS5 (R727G, V643A) and six non-synonymous SNVs on NHP sequences, in proteins E (L385F), NS1 (A171V), NS3 (I184V) and NS5 (N11S, I374V, E641D). We performed comparative protein structural analysis on these SNVs, describing possible impacts on protein function. Despite the fact that the dataset is limited in size and that this study does not consider virus-host interactions, our work highlights the use of machine learning as a versatile and fast initial approach to genomic data exploration.
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Affiliation(s)
- Álvaro Salgado
- Laboratório de Genética Celular e Molecular, Instituto de Ciências Biológicas, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil
- * E-mail: (AS); (LCJA); (JL)
| | - Raquel C. de Melo-Minardi
- Departamento de Ciência da Computação, Instituto de Ciências Exatas, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil
| | - Marta Giovanetti
- Laboratório de Genética Celular e Molecular, Instituto de Ciências Biológicas, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil
- Laboratório de Flavivírus, Instituto Oswaldo Cruz, Fundação Oswaldo Cruz, Rio de Janeiro, Brazil
| | - Adriano Veloso
- Departamento de Ciência da Computação, Instituto de Ciências Exatas, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil
| | - Francielly Morais-Rodrigues
- Laboratório de Genética Celular e Molecular, Instituto de Ciências Biológicas, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil
| | - Talita Adelino
- Laboratório Central de Saúde Pública, Fundação Ezequiel Dias, Belo Horizonte, Minas Gerais, Brazil
| | - Ronaldo de Jesus
- Coordenação Geral dos Laboratórios de Saúde Pública, Secretaria de Vigilância em Saúde, Ministério da Saúde, Brasília, DF, Brazil
| | - Stephane Tosta
- Laboratório de Genética Celular e Molecular, Instituto de Ciências Biológicas, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil
| | - Vasco Azevedo
- Laboratório de Genética Celular e Molecular, Instituto de Ciências Biológicas, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil
| | - José Lourenco
- Department of Zoology, University of Oxford, Oxford, United Kingdom
- * E-mail: (AS); (LCJA); (JL)
| | - Luiz Carlos J. Alcantara
- Laboratório de Genética Celular e Molecular, Instituto de Ciências Biológicas, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil
- Laboratório de Flavivírus, Instituto Oswaldo Cruz, Fundação Oswaldo Cruz, Rio de Janeiro, Brazil
- * E-mail: (AS); (LCJA); (JL)
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Feng D, Baumgartner R. A closer look at the kernels generated by the decision and regression tree ensembles. Stat Biopharm Res 2022. [DOI: 10.1080/19466315.2022.2150680] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Affiliation(s)
- Dai Feng
- Data and Statistical Sciences, AbbVie Inc., North Chicago, IL, USA
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36
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da Silva Muniz VH, de Oliveira e Souza Filho JB. Robust handcrafted features for music genre classification. Neural Comput Appl 2022. [DOI: 10.1007/s00521-022-08069-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
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37
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Reuse, Reduce, Support: Design Principles for Green Data Mining. BUSINESS & INFORMATION SYSTEMS ENGINEERING 2022. [DOI: 10.1007/s12599-022-00780-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
AbstractThis paper reports on a design science research (DSR) study that develops design principles for “green” – more environmentally sustainable – data mining processes. Grounded in the Cross Industry Standard Process for Data Mining (CRISP-DM) and on a review of relevant literature on data mining methods, Green IT, and Green IS, the study identifies eight design principles that fall into the three categories of reuse, reduce, and support. The paper develops an evaluation strategy and provides empirical evidence for the principles’ utility. It suggests that the results can inform the development of a more general approach towards Green Data Science and provide a suitable lens to study sustainable computing.
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Vijayakumar R, Choi JY, Jung EH. A Unified Neural Network Framework for Extended Redundancy Analysis. PSYCHOMETRIKA 2022; 87:1503-1528. [PMID: 35332421 DOI: 10.1007/s11336-022-09853-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/05/2019] [Revised: 01/25/2022] [Indexed: 06/14/2023]
Abstract
Component-based approaches have been regarded as a tool for dimension reduction to predict outcomes from observed variables in regression applications. Extended redundancy analysis (ERA) is one such component-based approach which reduces predictors to components explaining maximum variance in the outcome variables. In many instances, ERA can be extended to capture nonlinearity and interactions between observed and components, but only by specifying a priori functional form. Meanwhile, machine learning methods like neural networks are typically used in a data-driven manner to capture nonlinearity without specifying the exact functional form. In this paper, we introduce a new method that integrates neural networks algorithms into the framework of ERA, called NN-ERA, to capture any non-specified nonlinear relationships among multiple sets of observed variables for constructing components. Simulations and empirical datasets are used to demonstrate the usefulness of NN-ERA. The conclusion is that in social science datasets with unstructured data, where we expect nonlinear relationships that cannot be specified a priori, NN-ERA with its neural network algorithmic structure can serve as a useful tool to specify and test models otherwise not captured by the conventional component-based models.
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Affiliation(s)
- Ranjith Vijayakumar
- Department of Psychology, National University of Singapore, 9 Arts Link, Singapore, Singapore
| | - Ji Yeh Choi
- Department of Psychology, York University, 4700 Keele St., Toronto, ON, Canada.
| | - Eun Hwa Jung
- School of Media & Advertising, Kookmin University, 77 Jeongneung-Ro, Seongbuk-Gu, Seoul, South Korea
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Tran LV, Tran HM, Le TM, Huynh TTM, Tran HT, Dao SVT. Application of Machine Learning in Epileptic Seizure Detection. Diagnostics (Basel) 2022; 12:diagnostics12112879. [PMID: 36428941 PMCID: PMC9689720 DOI: 10.3390/diagnostics12112879] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2022] [Revised: 11/09/2022] [Accepted: 11/13/2022] [Indexed: 11/22/2022] Open
Abstract
Epileptic seizure is a neurological condition caused by short and unexpectedly occurring electrical disruptions in the brain. It is estimated that roughly 60 million individuals worldwide have had an epileptic seizure. Experiencing an epileptic seizure can have serious consequences for the patient. Automatic seizure detection on electroencephalogram (EEG) recordings is essential due to the irregular and unpredictable nature of seizures. By thoroughly analyzing EEG records, neurophysiologists can discover important information and patterns, and proper and timely treatments can be provided for the patients. This research presents a novel machine learning-based approach for detecting epileptic seizures in EEG signals. A public EEG dataset from the University of Bonn was used to validate the approach. Meaningful statistical features were extracted from the original data using discrete wavelet transform analysis, then the relevant features were selected using feature selection based on the binary particle swarm optimizer. This facilitated the reduction of 75% data dimensionality and 47% computational time, which eventually sped up the classification process. After having been selected, relevant features were used to train different machine learning models, then hyperparameter optimization was utilized to further enhance the models' performance. The results achieved up to 98.4% accuracy and showed that the proposed method was very effective and practical in detecting seizure presence in EEG signals. In clinical applications, this method could help relieve the suffering of epilepsy patients and alleviate the workload of neurologists.
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Affiliation(s)
- Ly V. Tran
- School of Industrial Engineering and Management, International University, Vietnam National University, Ho Chi Minh City 700000, Vietnam
| | - Hieu M. Tran
- School of Electrical Engineering, International University, Vietnam National University, Ho Chi Minh City 700000, Vietnam
| | - Tuan M. Le
- School of Electrical Engineering, International University, Vietnam National University, Ho Chi Minh City 700000, Vietnam
| | - Tri T. M. Huynh
- School of Electrical Engineering, International University, Vietnam National University, Ho Chi Minh City 700000, Vietnam
| | - Hung T. Tran
- School of Industrial Engineering and Management, International University, Vietnam National University, Ho Chi Minh City 700000, Vietnam
| | - Son V. T. Dao
- School of Industrial Engineering and Management, International University, Vietnam National University, Ho Chi Minh City 700000, Vietnam
- School of Science, Engineering & Technology, RMIT University Vietnam, Ho Chi Minh City 700000, Vietnam
- Correspondence: or ; Tel.: +84-98-159-1145
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Li C, Wang Y. Non-Targeted Analytical Technology in Herbal Medicines: Applications, Challenges, and Perspectives. Crit Rev Anal Chem 2022:1-20. [PMID: 36409298 DOI: 10.1080/10408347.2022.2148204] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Abstract
Herbal medicines (HMs) have been utilized to prevent and treat human ailments for thousands of years. Especially, HMs have recently played a crucial role in the treatment of COVID-19 in China. However, HMs are susceptible to various factors during harvesting, processing, and marketing, affecting their clinical efficacy. Therefore, it is necessary to conclude a rapid and effective method to study HMs so that they can be used in the clinical setting with maximum medicinal value. Non-targeted analytical technology is a reliable analytical method for studying HMs because of its unique advantages in analyzing unknown components. Based on the extensive literature, the paper summarizes the benefits, limitations, and applicability of non-targeted analytical technology. Moreover, the article describes the application of non-targeted analytical technology in HMs from four aspects: structure analysis, authentication, real-time monitoring, and quality assessment. Finally, the review has prospected the development trend and challenges of non-targeted analytical technology. It can assist HMs industry researchers and engineers select non-targeted analytical technology to analyze HMs' quality and authenticity.
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Affiliation(s)
- Chaoping Li
- Medicinal Plants Research Institute, Yunnan Academy of Agricultural Sciences, Kunming, China
- College of Traditional Chinese Medicine, Yunnan University of Chinese Medicine, Kunming, China
| | - Yuanzhong Wang
- Medicinal Plants Research Institute, Yunnan Academy of Agricultural Sciences, Kunming, China
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Multiple instance neural networks based on sparse attention for cancer detection using T-cell receptor sequences. BMC Bioinformatics 2022; 23:469. [PMID: 36348271 PMCID: PMC9644450 DOI: 10.1186/s12859-022-05012-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2022] [Accepted: 10/26/2022] [Indexed: 11/11/2022] Open
Abstract
Early detection of cancers has been much explored due to its paramount importance in biomedical fields. Among different types of data used to answer this biological question, studies based on T cell receptors (TCRs) are under recent spotlight due to the growing appreciation of the roles of the host immunity system in tumor biology. However, the one-to-many correspondence between a patient and multiple TCR sequences hinders researchers from simply adopting classical statistical/machine learning methods. There were recent attempts to model this type of data in the context of multiple instance learning (MIL). Despite the novel application of MIL to cancer detection using TCR sequences and the demonstrated adequate performance in several tumor types, there is still room for improvement, especially for certain cancer types. Furthermore, explainable neural network models are not fully investigated for this application. In this article, we propose multiple instance neural networks based on sparse attention (MINN-SA) to enhance the performance in cancer detection and explainability. The sparse attention structure drops out uninformative instances in each bag, achieving both interpretability and better predictive performance in combination with the skip connection. Our experiments show that MINN-SA yields the highest area under the ROC curve scores on average measured across 10 different types of cancers, compared to existing MIL approaches. Moreover, we observe from the estimated attentions that MINN-SA can identify the TCRs that are specific for tumor antigens in the same T cell repertoire.
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42
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Xu Y, Zhang X, Li H, Zheng H, Zhang J, Olsen MS, Varshney RK, Prasanna BM, Qian Q. Smart breeding driven by big data, artificial intelligence, and integrated genomic-enviromic prediction. MOLECULAR PLANT 2022; 15:1664-1695. [PMID: 36081348 DOI: 10.1016/j.molp.2022.09.001] [Citation(s) in RCA: 41] [Impact Index Per Article: 20.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/04/2022] [Revised: 08/20/2022] [Accepted: 09/02/2022] [Indexed: 05/12/2023]
Abstract
The first paradigm of plant breeding involves direct selection-based phenotypic observation, followed by predictive breeding using statistical models for quantitative traits constructed based on genetic experimental design and, more recently, by incorporation of molecular marker genotypes. However, plant performance or phenotype (P) is determined by the combined effects of genotype (G), envirotype (E), and genotype by environment interaction (GEI). Phenotypes can be predicted more precisely by training a model using data collected from multiple sources, including spatiotemporal omics (genomics, phenomics, and enviromics across time and space). Integration of 3D information profiles (G-P-E), each with multidimensionality, provides predictive breeding with both tremendous opportunities and great challenges. Here, we first review innovative technologies for predictive breeding. We then evaluate multidimensional information profiles that can be integrated with a predictive breeding strategy, particularly envirotypic data, which have largely been neglected in data collection and are nearly untouched in model construction. We propose a smart breeding scheme, integrated genomic-enviromic prediction (iGEP), as an extension of genomic prediction, using integrated multiomics information, big data technology, and artificial intelligence (mainly focused on machine and deep learning). We discuss how to implement iGEP, including spatiotemporal models, environmental indices, factorial and spatiotemporal structure of plant breeding data, and cross-species prediction. A strategy is then proposed for prediction-based crop redesign at both the macro (individual, population, and species) and micro (gene, metabolism, and network) scales. Finally, we provide perspectives on translating smart breeding into genetic gain through integrative breeding platforms and open-source breeding initiatives. We call for coordinated efforts in smart breeding through iGEP, institutional partnerships, and innovative technological support.
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Affiliation(s)
- Yunbi Xu
- Institute of Crop Sciences, CIMMYT-China, Chinese Academy of Agricultural Sciences, Beijing 100081, China; CIMMYT-China Tropical Maize Research Center, School of Food Science and Engineering, Foshan University, Foshan, Guangdong 528231, China; Peking University Institute of Advanced Agricultural Sciences, Weifang, Shandong 261325, China.
| | - Xingping Zhang
- Peking University Institute of Advanced Agricultural Sciences, Weifang, Shandong 261325, China
| | - Huihui Li
- Institute of Crop Sciences, CIMMYT-China, Chinese Academy of Agricultural Sciences, Beijing 100081, China; National Nanfan Research Institute (Sanya), Chinese Academy of Agricultural Sciences, Sanya, Hainan 572024, China
| | - Hongjian Zheng
- CIMMYT-China Specialty Maize Research Center, Shanghai Academy of Agricultural Sciences, Shanghai 201400, China
| | - Jianan Zhang
- MolBreeding Biotechnology Co., Ltd., Shijiazhuang, Hebei 050035, China
| | - Michael S Olsen
- CIMMYT (International Maize and Wheat Improvement Center), ICRAF Campus, United Nations Avenue, Nairobi, Kenya
| | - Rajeev K Varshney
- State Agricultural Biotechnology Centre, Centre for Crop and Food Innovation, Food Futures Institute, Murdoch University, Murdoch, Australia
| | - Boddupalli M Prasanna
- CIMMYT (International Maize and Wheat Improvement Center), ICRAF Campus, United Nations Avenue, Nairobi, Kenya
| | - Qian Qian
- Institute of Crop Sciences, CIMMYT-China, Chinese Academy of Agricultural Sciences, Beijing 100081, China
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43
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A Genetically-optimised Artificial Life Algorithm for Complexity-based Synthetic Dataset Generation. Inf Sci (N Y) 2022. [DOI: 10.1016/j.ins.2022.11.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022]
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44
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Schuhmacher D, Schörner S, Küpper C, Großerueschkamp F, Sternemann C, Lugnier C, Kraeft AL, Jütte H, Tannapfel A, Reinacher-Schick A, Gerwert K, Mosig A. A framework for falsifiable explanations of machine learning models with an application in computational pathology. Med Image Anal 2022; 82:102594. [DOI: 10.1016/j.media.2022.102594] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2021] [Revised: 08/11/2022] [Accepted: 08/18/2022] [Indexed: 10/31/2022]
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Kaveh M, Mesgari MS. Application of Meta-Heuristic Algorithms for Training Neural Networks and Deep Learning Architectures: A Comprehensive Review. Neural Process Lett 2022; 55:1-104. [PMID: 36339645 PMCID: PMC9628382 DOI: 10.1007/s11063-022-11055-6] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/11/2022] [Indexed: 12/02/2022]
Abstract
The learning process and hyper-parameter optimization of artificial neural networks (ANNs) and deep learning (DL) architectures is considered one of the most challenging machine learning problems. Several past studies have used gradient-based back propagation methods to train DL architectures. However, gradient-based methods have major drawbacks such as stucking at local minimums in multi-objective cost functions, expensive execution time due to calculating gradient information with thousands of iterations and needing the cost functions to be continuous. Since training the ANNs and DLs is an NP-hard optimization problem, their structure and parameters optimization using the meta-heuristic (MH) algorithms has been considerably raised. MH algorithms can accurately formulate the optimal estimation of DL components (such as hyper-parameter, weights, number of layers, number of neurons, learning rate, etc.). This paper provides a comprehensive review of the optimization of ANNs and DLs using MH algorithms. In this paper, we have reviewed the latest developments in the use of MH algorithms in the DL and ANN methods, presented their disadvantages and advantages, and pointed out some research directions to fill the gaps between MHs and DL methods. Moreover, it has been explained that the evolutionary hybrid architecture still has limited applicability in the literature. Also, this paper classifies the latest MH algorithms in the literature to demonstrate their effectiveness in DL and ANN training for various applications. Most researchers tend to extend novel hybrid algorithms by combining MHs to optimize the hyper-parameters of DLs and ANNs. The development of hybrid MHs helps improving algorithms performance and capable of solving complex optimization problems. In general, the optimal performance of the MHs should be able to achieve a suitable trade-off between exploration and exploitation features. Hence, this paper tries to summarize various MH algorithms in terms of the convergence trend, exploration, exploitation, and the ability to avoid local minima. The integration of MH with DLs is expected to accelerate the training process in the coming few years. However, relevant publications in this way are still rare.
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Affiliation(s)
- Mehrdad Kaveh
- Department of Geodesy and Geomatics, K. N. Toosi University of Technology, Tehran, 19967-15433 Iran
| | - Mohammad Saadi Mesgari
- Department of Geodesy and Geomatics, K. N. Toosi University of Technology, Tehran, 19967-15433 Iran
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Reshma IA, Franchet C, Gaspard M, Ionescu RT, Mothe J, Cussat-Blanc S, Luga H, Brousset P. Finding a Suitable Class Distribution for Building Histological Images Datasets Used in Deep Model Training-The Case of Cancer Detection. J Digit Imaging 2022; 35:1326-1349. [PMID: 35445341 PMCID: PMC9582112 DOI: 10.1007/s10278-022-00618-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2020] [Revised: 02/15/2022] [Accepted: 03/09/2022] [Indexed: 11/26/2022] Open
Abstract
The class distribution of a training dataset is an important factor which influences the performance of a deep learning-based system. Understanding the optimal class distribution is therefore crucial when building a new training set which may be costly to annotate. This is the case for histological images used in cancer diagnosis where image annotation requires domain experts. In this paper, we tackle the problem of finding the optimal class distribution of a training set to be able to train an optimal model that detects cancer in histological images. We formulate several hypotheses which are then tested in scores of experiments with hundreds of trials. The experiments have been designed to account for both segmentation and classification frameworks with various class distributions in the training set, such as natural, balanced, over-represented cancer, and over-represented non-cancer. In the case of cancer detection, the experiments show several important results: (a) the natural class distribution produces more accurate results than the artificially generated balanced distribution; (b) the over-representation of non-cancer/negative classes (healthy tissue and/or background classes) compared to cancer/positive classes reduces the number of samples which are falsely predicted as cancer (false positive); (c) the least expensive to annotate non-ROI (non-region-of-interest) data can be useful in compensating for the performance loss in the system due to a shortage of expensive to annotate ROI data; (d) the multi-label examples are more useful than the single-label ones to train a segmentation model; and (e) when the classification model is tuned with a balanced validation set, it is less affected than the segmentation model by the class distribution of the training set.
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Affiliation(s)
| | - Camille Franchet
- Department of Pathology, University Cancer Institute of Toulouse-Oncopole, Toulouse, France
| | - Margot Gaspard
- Department of Pathology, University Cancer Institute of Toulouse-Oncopole, Toulouse, France
| | | | - Josiane Mothe
- IRIT, UMR5505 CNRS, Université de Toulouse, Toulouse, France
| | - Sylvain Cussat-Blanc
- IRIT, UMR5505 CNRS, Université de Toulouse, Toulouse, France
- Artificial and Natural Intelligence Toulouse Institute, Toulouse, France
| | - Hervé Luga
- IRIT, UMR5505 CNRS, Université de Toulouse, Toulouse, France
| | - Pierre Brousset
- Department of Pathology, University Cancer Institute of Toulouse-Oncopole, Toulouse, France
- INSERM UMR 1037 Cancer Research Centre of Toulouse (CRCT), Université Toulouse III Paul-Sabatier, CNRS ERL 5294, Toulouse, France
- Laboratoire d’Excellence TOUCAN, Toulouse, France
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Nascimben M, Rimondini L, Corà D, Venturin M. Polygenic risk modeling of tumor stage and survival in bladder cancer. BioData Min 2022; 15:23. [PMID: 36175974 PMCID: PMC9523990 DOI: 10.1186/s13040-022-00306-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2021] [Accepted: 09/18/2022] [Indexed: 11/26/2022] Open
Abstract
Introduction Bladder cancer assessment with non-invasive gene expression signatures facilitates the detection of patients at risk and surveillance of their status, bypassing the discomforts given by cystoscopy. To achieve accurate cancer estimation, analysis pipelines for gene expression data (GED) may integrate a sequence of several machine learning and bio-statistical techniques to model complex characteristics of pathological patterns. Methods Numerical experiments tested the combination of GED preprocessing by discretization with tree ensemble embeddings and nonlinear dimensionality reductions to categorize oncological patients comprehensively. Modeling aimed to identify tumor stage and distinguish survival outcomes in two situations: complete and partial data embedding. This latter experimental condition simulates the addition of new patients to an existing model for rapid monitoring of disease progression. Machine learning procedures were employed to identify the most relevant genes involved in patient prognosis and test the performance of preprocessed GED compared to untransformed data in predicting patient conditions. Results Data embedding paired with dimensionality reduction produced prognostic maps with well-defined clusters of patients, suitable for medical decision support. A second experiment simulated the addition of new patients to an existing model (partial data embedding): Uniform Manifold Approximation and Projection (UMAP) methodology with uniform data discretization led to better outcomes than other analyzed pipelines. Further exploration of parameter space for UMAP and t-distributed stochastic neighbor embedding (t-SNE) underlined the importance of tuning a higher number of parameters for UMAP rather than t-SNE. Moreover, two different machine learning experiments identified a group of genes valuable for partitioning patients (gene relevance analysis) and showed the higher precision obtained by preprocessed data in predicting tumor outcomes for cancer stage and survival rate (six classes prediction). Conclusions The present investigation proposed new analysis pipelines for disease outcome modeling from bladder cancer-related biomarkers. Complete and partial data embedding experiments suggested that pipelines employing UMAP had a more accurate predictive ability, supporting the recent literature trends on this methodology. However, it was also found that several UMAP parameters influence experimental results, therefore deriving a recommendation for researchers to pay attention to this aspect of the UMAP technique. Machine learning procedures further demonstrated the effectiveness of the proposed preprocessing in predicting patients’ conditions and determined a sub-group of biomarkers significant for forecasting bladder cancer prognosis.
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Affiliation(s)
- Mauro Nascimben
- Department of Health Sciences, Università del Piemonte Orientale, Via Solaroli 17, 28100, Novara, Italy. .,Enginsoft SpA, Via Giambellino 7, 35129, Padova, Italy.
| | - Lia Rimondini
- Department of Health Sciences, Università del Piemonte Orientale, Via Solaroli 17, 28100, Novara, Italy
| | - Davide Corà
- Department of Health Sciences, Università del Piemonte Orientale, Via Solaroli 17, 28100, Novara, Italy.,Department of Translational Medicine, Università del Piemonte Orientale, Via Solaroli 17, 28100, Novara, Italy
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Pouncy T, Gershman SJ. Inductive biases in theory-based reinforcement learning. Cogn Psychol 2022; 138:101509. [PMID: 36152355 DOI: 10.1016/j.cogpsych.2022.101509] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2022] [Revised: 07/16/2022] [Accepted: 08/23/2022] [Indexed: 11/03/2022]
Abstract
Understanding the inductive biases that allow humans to learn in complex environments has been an important goal of cognitive science. Yet, while we have discovered much about human biases in specific learning domains, much of this research has focused on simple tasks that lack the complexity of the real world. In contrast, video games involving agents and objects embedded in richly structured systems provide an experimentally tractable proxy for real-world complexity. Recent work has suggested that key aspects of human learning in domains like video games can be captured by model-based reinforcement learning (RL) with object-oriented relational models-what we term theory-based RL. Restricting the model class in this way provides an inductive bias that dramatically increases learning efficiency, but in this paper we show that humans employ a stronger set of biases in addition to syntactic constraints on the structure of theories. In particular, we catalog a set of semantic biases that constrain the content of theories. Building these semantic biases into a theory-based RL system produces more human-like learning in video game environments.
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Affiliation(s)
- Thomas Pouncy
- Department of Psychology and Center for Brain Science, Harvard University, United States of America.
| | - Samuel J Gershman
- Department of Psychology and Center for Brain Science, Harvard University, United States of America; Center for Brains, Minds and Machines, MIT, United States of America
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van Spanning SH, Verweij LPE, Allaart LJH, Hendrickx LAM, Doornberg JN, Athwal GS, Lafosse T, Lafosse L, van den Bekerom MPJ, Buijze GA. Development and training of a machine learning algorithm to identify patients at risk for recurrence following an arthroscopic Bankart repair (CLEARER): protocol for a retrospective, multicentre, cohort study. BMJ Open 2022; 12:e055346. [PMID: 36508223 PMCID: PMC9462090 DOI: 10.1136/bmjopen-2021-055346] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Abstract
INTRODUCTION Shoulder instability is a common injury, with a reported incidence of 23.9 per 100 000 person-years. There is still an ongoing debate on the most effective treatment strategy. Non-operative treatment has recurrence rates of up to 60%, whereas operative treatments such as the Bankart repair and bone block procedures show lower recurrence rates (16% and 2%, respectively) but higher complication rates (<2% and up to 30%, respectively). Methods to determine risk of recurrence have been developed; however, patient-specific decision-making tools are still lacking. Artificial intelligence and machine learning algorithms use self-learning complex models that can be used to make patient-specific decision-making tools. The aim of the current study is to develop and train a machine learning algorithm to create a prediction model to be used in clinical practice-as an online prediction tool-to estimate recurrence rates following a Bankart repair. METHODS AND ANALYSIS This is a multicentre retrospective cohort study. Patients with traumatic anterior shoulder dislocations that were treated with an arthroscopic Bankart repair without remplissage will be included. This study includes two parts. Part 1, collecting all potential factors influencing the recurrence rate following an arthroscopic Bankart repair in patients using multicentre data, aiming to include data from >1000 patients worldwide. Part 2, the multicentre data will be re-evaluated (and where applicable complemented) using machine learning algorithms to predict outcomes. Recurrence will be the primary outcome measure. ETHICS AND DISSEMINATION For safe multicentre data exchange and analysis, our Machine Learning Consortium adhered to the WHO regulation 'Policy on Use and Sharing of Data Collected by WHO in Member States Outside the Context of Public Health Emergencies'. The study results will be disseminated through publication in a peer-reviewed journal. No Institutional Review Board is required for this study.
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Affiliation(s)
- Sanne H van Spanning
- Orthopaedic Surgery, OLVG, Amsterdam, Noord-Holland, The Netherlands
- Hand, Upper Limb, Peripheral Nerve, Brachial Plexus and Microsurgery Unit, Alps Surgery Institute, Annecy, France
- Department of Human Movement Sciences, Faculty of Behavioural and Movement Sciences, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Lukas P E Verweij
- Orthopedic Surgery, Amsterdam Movement Sciences, Amsterdam UMC Locatie AMC, Amsterdam, North Holland, The Netherlands
- Academic Center for Evidence-based Sports Medicine (ACES), Amsterdam UMC Locatie AMC, Amsterdam, North Holland, The Netherlands
- Amsterdam Collaboration for Health and Safety in Sports (ACHSS), International Olympic Committee (IOC) Research Centre, Amsterdam UMC, Amsterdam, Netherlands
| | - Laurens J H Allaart
- Hand, Upper Limb, Peripheral Nerve, Brachial Plexus and Microsurgery Unit, Alps Surgery Institute, Annecy, France
- Department of Human Movement Sciences, Faculty of Behavioural and Movement Sciences, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Laurent A M Hendrickx
- Orthopedic Surgery, Amsterdam Movement Sciences, Amsterdam UMC Locatie AMC, Amsterdam, North Holland, The Netherlands
- Academic Center for Evidence-based Sports Medicine (ACES), Amsterdam UMC Locatie AMC, Amsterdam, North Holland, The Netherlands
- Department of Orthopaedic & Trauma Surgery, Flinders Medical Centre, Flinders University, Adelaide, South Australia, Australia
| | - Job N Doornberg
- Department of Orthopaedic & Trauma Surgery, Flinders Medical Centre, Flinders University, Adelaide, South Australia, Australia
| | - George S Athwal
- Roth McFarlane Hand and Upper Limb Center, Schulich School of Medicine and Dentistry, London, Ontario, Canada
| | - Thibault Lafosse
- Hand, Upper Limb, Peripheral Nerve, Brachial Plexus and Microsurgery Unit, Alps Surgery Institute, Annecy, France
| | - Laurent Lafosse
- Hand, Upper Limb, Peripheral Nerve, Brachial Plexus and Microsurgery Unit, Alps Surgery Institute, Annecy, France
| | - Michel P J van den Bekerom
- Orthopaedic Surgery, OLVG, Amsterdam, Noord-Holland, The Netherlands
- Department of Human Movement Sciences, Faculty of Behavioural and Movement Sciences, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Geert Alexander Buijze
- Hand, Upper Limb, Peripheral Nerve, Brachial Plexus and Microsurgery Unit, Alps Surgery Institute, Annecy, France
- Orthopedic Surgery, Amsterdam Movement Sciences, Amsterdam UMC Locatie AMC, Amsterdam, North Holland, The Netherlands
- Department of Orthopaedic Surgery, Montpellier University Medical Center, Montpellier, Languedoc-Roussillon, France
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Montesinos-López OA, Gonzalez HN, Montesinos-López A, Daza-Torres M, Lillemo M, Montesinos-López JC, Crossa J. Comparing gradient boosting machine and Bayesian threshold BLUP for genome-based prediction of categorical traits in wheat breeding. THE PLANT GENOME 2022; 15:e20214. [PMID: 35535459 DOI: 10.1002/tpg2.20214] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/05/2022] [Accepted: 03/21/2022] [Indexed: 06/14/2023]
Abstract
Genomic selection (GS) is a predictive methodology that is changing plant breeding. Genomic selection trains a statistical machine-learning model using available phenotypic and genotypic data with which predictions are performed for individuals that were only genotyped. For this reason, some statistical machine-learning methods are being implemented in GS, but in order to improve the selection of new genotypes early in the prediction process, the exploration of new statistical machine-learning algorithms must continue. In this paper, we performed a benchmarking study between the Bayesian threshold genomic best linear unbiased predictor model (TGBLUP; popular in GS) and the gradient boosting machine (GBM). This comparison was done using four real wheat (Triticum aestivum L.) data sets with categorical traits measured in terms of two metrics: the proportion of cases correctly classified (PCCC) and the Kappa coefficient in the testing set. Under 10 random partitions with four different sizes of testing proportions (20, 40, 60, and 80%), we compared the two algorithms and found that in three of the four data sets, the GBM outperformed the TGBLUP model in terms of both metrics (PCCC and Kappa coefficient). In the larger data sets (Data Sets 3 and 4), the gain in terms of prediction accuracy of the GBM was considerably significant. For this reason, we encourage more research using the GBM in GS to evaluate its virtues in terms of prediction performance in the context of GS.
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Affiliation(s)
| | | | - Abelardo Montesinos-López
- Centro Universitario de Ciencias Exactas e Ingenierías (CUCEI), Univ. de Guadalajara, Guadalajara, Jalisco, 44430, México
| | - María Daza-Torres
- Dep. of Public Health Sciences, Univ. of California, Davis, CA, 95616, USA
| | - Morten Lillemo
- Dep. of Plant Sciences, Norwegian Univ. of Life Sciences, IHA/CIGENE, P.O. Box 5003, NO-1432, Ås, Norway
| | | | - José Crossa
- Colegio de Postgraduados, Montecillos, Edo. de México, 56230, México
- Biometrics and Statistics Unit, Genetic Resources Program, International Maize and Wheat Improvement Center (CIMMYT), Km 45, Carretera, México-Veracruz, 52640, México
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