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Hang R, Yao X, Bai L, Hang R. Evolving biomaterials design from trial and error to intelligent innovation. Acta Biomater 2025; 197:29-47. [PMID: 40081552 DOI: 10.1016/j.actbio.2025.03.013] [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: 11/20/2024] [Revised: 01/20/2025] [Accepted: 03/06/2025] [Indexed: 03/16/2025]
Abstract
The design and exploration of biomaterials plays a pivotal role in many fields, including medical and engineering. The prevailing approach to biomaterials discovery relies on orthogonal experiments, with repeated attempts to optimize experimental conditions. This method has proven invaluable in gaining experience, but it is also inefficient and challenging to predict the behavior of complex systems. The advent of high-throughput screening (HTS) techniques has led to a notable enhancement in the efficiency of biomaterials development, enabling researchers to assess a vast array of material combinations within a relatively short timeframe. Nevertheless, the emergence of artificial intelligence (AI) has been the catalyst for a new era in biomaterials design. AI has markedly accelerated the development of new materials by enabling the prediction of material properties through machine learning (ML) and deep learning models, as well as optimizing the design pipeline. This review will present a systematic overview of the development of biomaterials design technology. It will also explore the integration of AI with HTS technology and envisage the potential of AI-driven materials design in biomaterials for the future. STATEMENT OF SIGNIFICANCE: The design and synthesis of biomaterials have undergone substantial shifts, reflecting evolving research paradigms. High-throughput screening has emerged as a broad and efficient alternative to traditional free-form combination methods in biomaterial design. The advent of artificial intelligence (AI) enables personalized biomaterial design and, as a transformative tool in biomaterial development, is poised to redefine the field and offer long-term solutions for its advancement. Building on these advancements, this review systematically summarizes the evolution of biomaterial design, offering insights into the future trajectory of the field.
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Affiliation(s)
- Ruiyue Hang
- Shanxi Key Laboratory of Biomedical Metal Materials, College of Materials Science and Engineering, Taiyuan University of Technology, Taiyuan 030024, PR China
| | - Xiaohong Yao
- Shanxi Key Laboratory of Biomedical Metal Materials, College of Materials Science and Engineering, Taiyuan University of Technology, Taiyuan 030024, PR China
| | - Long Bai
- Institute of Translational Medicine, Shanghai University, Shanghai, 200444, PR China.
| | - Ruiqiang Hang
- Shanxi Key Laboratory of Biomedical Metal Materials, College of Materials Science and Engineering, Taiyuan University of Technology, Taiyuan 030024, PR China.
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2
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Chen X, Gu Z, Esposito L, Lv J. Overview of rural credit environment in China: Measurement logic, evaluation system, and case analysis. EVALUATION AND PROGRAM PLANNING 2025; 108:102519. [PMID: 39566296 DOI: 10.1016/j.evalprogplan.2024.102519] [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: 09/29/2024] [Revised: 10/21/2024] [Accepted: 11/12/2024] [Indexed: 11/22/2024]
Abstract
A rational evaluation of the rural credit environment's current state and identification of its critical issues are crucial for enhancing the construction of rural social credit systems. Drawing on the "Outline for the Construction of the Social Credit System (2014-2020)" and related literature, this paper clarifies the concepts and measurement principles of the rural credit environment. This study innovatively constructs an evaluation framework for the rural credit environment and conducts quantitative measurements and statistical analyses using a combined weighting method to delineate the environment's current state. Using Banqiao Town as a case study, this paper employs the cloud model to evaluate the rural credit environment's quality levels and to identify key factors influencing its quality effectively. From a macro perspective, statistical measurements and index analyses establish evaluation standards and quantify the state of the rural credit environment. In contrast, from a micro perspective, case analysis focuses on evaluating the quality levels and identifying crucial issues in specific locales. Integrating macro and micro perspectives offers a novel approach to evaluating the rural credit environment, offering theoretical approaches and practical strategies for promoting a favorable credit environment and advancing the construction of rural social credit systems in China.
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Affiliation(s)
- Xihui Chen
- School of Management, Zhejiang University of Technology, Hangzhou, Zhejiang 310000, China; Innovation and Development Research Center, Hangzhou United Rural Commercial Bank, Hangzhou 310000, China
| | - Zhouyi Gu
- School of Information Technology, Zhejiang Financial College, Hangzhou 310000, China.
| | - Luca Esposito
- Karelian Institute, University of Eastern Finland, Joensuu 80100, Finland; Department of Economics and Statistics, University of Salerno, Salerno 84084, Italy
| | - Jiayan Lv
- Library, Huzhou University, Huzhou 313000, China
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3
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Qu X, Jiang C, Shan M, Ke W, Chen J, Zhao Q, Hu Y, Liu J, Qin LP, Cheng G. Prediction of Proteolysis-Targeting Chimeras Retention Time Using XGBoost Model Incorporated with Chromatographic Conditions. J Chem Inf Model 2025; 65:613-625. [PMID: 39786356 DOI: 10.1021/acs.jcim.4c01732] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2025]
Abstract
Proteolysis-targeting chimeras (PROTACs) are heterobifunctional molecules that target undruggable proteins, enhance selectivity and prevent target accumulation through catalytic activity. The unique structure of PROTACs presents challenges in structural identification and drug design. Liquid chromatography (LC), combined with mass spectrometry (MS), enhances compound annotation by providing essential retention time (RT) data, especially when MS alone is insufficient. However, predicting RT for PROTACs remains challenging. To address this, we compiled the PROTAC-RT data set from literature and evaluated the performance of four machine learning algorithms─extreme gradient boosting (XGBoost), random forest (RF), K-nearest neighbor (KNN) and support vector machines (SVM)─and a deep learning model, fully connected neural network (FCNN), using 24 molecular fingerprints and descriptors. Through screening combinations of molecular fingerprints, descriptors and chromatographic condition descriptors (CCs), we developed an optimized XGBoost model (XGBoost + moe206+Path + Charge + CCs) that achieved an R2 of 0.958 ± 0.027 and an RMSE of 0.934 ± 0.412. After hyperparameter tuning, the model's R2 improved to 0.963 ± 0.023, with an RMSE of 0.896 ± 0.374. The model showed strong predictive accuracy under new chromatographic separation conditions and was validated using six experimentally determined compounds. SHapley Additive exPlanations (SHAP) not only highlights the advantages of XGBoost but also emphasizes the importance of CCs and molecular features, such as bond variability, van der Waals surface area, and atomic charge states. The optimized XGBoost model combines moe206, path, charge descriptors, and CCs, providing a fast and precise method for predicting the RT of PROTACs compounds, thus facilitating their annotation.
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Affiliation(s)
- Xinhao Qu
- School of Pharmaceutical Sciences, Zhejiang Chinese Medical University, Hangzhou 310053, People's Republic of China
| | - Chen Jiang
- School of Pharmaceutical Sciences, Zhejiang Chinese Medical University, Hangzhou 310053, People's Republic of China
- Universal Identification Technology (Hangzhou) Co., Ltd., Hangzhou 311199, China
| | - Mengyi Shan
- School of Pharmaceutical Sciences, Zhejiang Chinese Medical University, Hangzhou 310053, People's Republic of China
| | - Wenhao Ke
- School of Pharmaceutical Science and Technology, Hangzhou Institute for Advanced Study, UCAS, 1 Xiangshanzhi Road, Hangzhou 310024, China
| | - Jing Chen
- School of Pharmaceutical Sciences, Zhejiang Chinese Medical University, Hangzhou 310053, People's Republic of China
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, People's Republic of China
| | - Qiming Zhao
- School of Pharmaceutical Sciences, Zhejiang Chinese Medical University, Hangzhou 310053, People's Republic of China
| | - Youhong Hu
- School of Pharmaceutical Science and Technology, Hangzhou Institute for Advanced Study, UCAS, 1 Xiangshanzhi Road, Hangzhou 310024, China
| | - Jia Liu
- School of Pharmaceutical Science and Technology, Hangzhou Institute for Advanced Study, UCAS, 1 Xiangshanzhi Road, Hangzhou 310024, China
| | - Lu-Ping Qin
- School of Pharmaceutical Sciences, Zhejiang Chinese Medical University, Hangzhou 310053, People's Republic of China
| | - Gang Cheng
- School of Pharmaceutical Sciences, Zhejiang Chinese Medical University, Hangzhou 310053, People's Republic of China
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Kumar LK, Suma KG, Udayaraju P, Gundu V, Mantena SV, Jagadesh BN. Clustering-based binary Grey Wolf Optimisation model with 6LDCNNet for prediction of heart disease using patient data. Sci Rep 2025; 15:1270. [PMID: 39779935 PMCID: PMC11711402 DOI: 10.1038/s41598-025-85561-7] [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: 12/08/2023] [Accepted: 01/03/2025] [Indexed: 01/11/2025] Open
Abstract
In recent years, the healthcare data system has expanded rapidly, allowing for the identification of important health trends and facilitating targeted preventative care. Heart disease remains a leading cause of death in developed countries, often leading to consequential outcomes such as dementia, which can be mitigated through early detection and treatment of cardiovascular issues. Continued research into preventing strokes and heart attacks is crucial. Utilizing the wealth of healthcare data related to cardiac ailments, a two-stage medical data classification and prediction model is proposed in this study. Initially, Binary Grey Wolf Optimization (BGWO) is used to cluster features, with the grouped information then utilized as input for the prediction model. An innovative 6-layered deep convolutional neural network (6LDCNNet) is designed for the classification of cardiac conditions. Hyper-parameter tuning for 6LDCNNet is achieved through an improved optimization method. The resulting model demonstrates promising performance on both the Cleveland dataset, achieving a convergence of 96% for assessing severity, and the echocardiography imaging dataset, with an impressive 98% convergence. This approach has the potential to aid physicians in diagnosing the severity of cardiac diseases, facilitating early interventions that can significantly reduce mortality associated with cardiovascular conditions.
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Affiliation(s)
- Lella Kranthi Kumar
- School of Computer Science and Engineering, VIT-AP University, Vijayawada, India.
| | - K G Suma
- School of Computer Science and Engineering, VIT-AP University, Vijayawada, India
| | - Pamula Udayaraju
- Department of Computer Science and Engineering, School of Engineering and Sciences, SRM University, Amaravati, AP, India
| | - Venkateswarlu Gundu
- Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, 522302, India
| | - Srihari Varma Mantena
- Department of Computer Science and Engineering, SRKR Engineering College, Bhimavaram, 534204, India
| | - B N Jagadesh
- School of Computer Science and Engineering, VIT-AP University, Vijayawada, India
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Banumathy D, Vetriselvi T, Venkatachalam K, Cho J. Quantum-inspired seagull optimised deep belief network approach for cardiovascular disease prediction. PeerJ Comput Sci 2024; 10:e2498. [PMID: 39896409 PMCID: PMC11784802 DOI: 10.7717/peerj-cs.2498] [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: 08/28/2024] [Accepted: 10/18/2024] [Indexed: 02/04/2025]
Abstract
The early detection and accurate diagnosis of cardiovascular diseases is vital to reduce global morbidity and death rates. In this work, the quantum-inspired seagull optimization algorithm (QISOA) combined with a deep belief network (DBN) is proposed to improve the identification of cardiovascular disorders. As part of preprocessing, cleaning, transformation, and standardization are performed to eliminate noise, inconsistencies, and scaling issues in the data. QISOA is used to optimize the weights and biases of the DBN model, enhancing its prediction efficiency. The algorithm incorporates quantum mechanics concepts to develop its exploration potential further, leading to faster convergence and increased global search efficiency. Optimized DBN provides efficient acquisition of hierarchical representations of the data, which results in improved feature learning and classification accuracy. The publicly accessible Cleveland Heart Disease dataset is used to assess the performance of the suggested model. Extensive experiments are conducted to demonstrate the superior performance of the QISOA-optimized DBN model compared to traditional machine learning and other metaheuristic-based models. Initially, machine learning models such as support vector machines, decision trees, Random Forests, multi-layer perceptrons, and fully connected networks were considered for comparison with the cardiovascular predictive performance of the DBN model. Further, meta-heuristic optimization algorithms such as particle swarm optimization, genetic algorithm, grey wolf optimization, cuckoo search optimization and crow search algorithm are combined with the machine learning models and the classification efficiency is evaluated. Additionally, few state-of-the-art techniques proposed in the existing literature are investigated and compared against the proposed model. It was evident from the comprehensive performance assessment of the proposed model that it yields a higher accuracy of 98.6% with precision, recall, and F1-scores of 97.6%, 96.8%, and 97.1%, respectively, compared to other traditional and existing models for cardiovascular disease prediction.
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Affiliation(s)
- D. Banumathy
- Department of Computer Science and Engineering, Paavai Engineering College, Namakkal, India
| | - T. Vetriselvi
- School of Computer science and Engineering, VIT University, Vellore, India
| | - K. Venkatachalam
- Department of Software Engineering, Jeonbuk National University, Jeonju-si, Republic of South Korea
| | - Jaehyuk Cho
- Department of Software Engineering & Division of Electronics and Information Engineering, Jeonbuk National University, Jeonju-si, Republic of South Korea
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Liu X, Zhu H, Zhang H, Xia S. The Framework of Quantifying Biomarkers of OCT and OCTA Images in Retinal Diseases. SENSORS (BASEL, SWITZERLAND) 2024; 24:5227. [PMID: 39204923 PMCID: PMC11359948 DOI: 10.3390/s24165227] [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: 07/05/2024] [Revised: 08/01/2024] [Accepted: 08/10/2024] [Indexed: 09/04/2024]
Abstract
Despite the significant advancements facilitated by previous research in introducing a plethora of retinal biomarkers, there is a lack of research addressing the clinical need for quantifying different biomarkers and prioritizing their importance for guiding clinical decision making in the context of retinal diseases. To address this issue, our study introduces a novel framework for quantifying biomarkers derived from optical coherence tomography (OCT) and optical coherence tomography angiography (OCTA) images in retinal diseases. We extract 452 feature parameters from five feature types, including local binary patterns (LBP) features of OCT and OCTA, capillary and large vessel features, and the foveal avascular zone (FAZ) feature. Leveraging this extensive feature set, we construct a classification model using a statistically relevant p value for feature selection to predict retinal diseases. We obtain a high accuracy of 0.912 and F1-score of 0.906 in the task of disease classification using this framework. We find that OCT and OCTA's LBP features provide a significant contribution of 77.12% to the significance of biomarkers in predicting retinal diseases, suggesting their potential as latent indicators for clinical diagnosis. This study employs a quantitative analysis framework to identify potential biomarkers for retinal diseases in OCT and OCTA images. Our findings suggest that LBP parameters, skewness and kurtosis values of capillary, the maximum, mean, median, and standard deviation of large vessel, as well as the eccentricity, compactness, flatness, and anisotropy index of FAZ, may serve as significant indicators of retinal conditions.
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Affiliation(s)
- Xiaoli Liu
- School of Biological Science and Medical Engineering, Beihang University, Beijing 100191, China
| | - Haogang Zhu
- Hangzhou International Innovation Institute, Beihang University, Beijing 100191, China
| | - Hanji Zhang
- School of Medical Technology, Tianjin Medical University, Tianjin 300203, China
| | - Shaoyan Xia
- School of Medical Technology, Tianjin Medical University, Tianjin 300203, China
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Wang J, Xue Q, Zhang CWJ, Wong KKL, Liu Z. Explainable coronary artery disease prediction model based on AutoGluon from AutoML framework. Front Cardiovasc Med 2024; 11:1360548. [PMID: 39011494 PMCID: PMC11246996 DOI: 10.3389/fcvm.2024.1360548] [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: 12/23/2023] [Accepted: 06/11/2024] [Indexed: 07/17/2024] Open
Abstract
Objective This study focuses on the innovative application of Automated Machine Learning (AutoML) technology in cardiovascular medicine to construct an explainable Coronary Artery Disease (CAD) prediction model to support the clinical diagnosis of CAD. Methods This study utilizes a combined data set of five public data sets related to CAD. An ensemble model is constructed using the AutoML open-source framework AutoGluon to evaluate the feasibility of AutoML in constructing a disease prediction model in cardiovascular medicine. The performance of the ensemble model is compared against individual baseline models. Finally, the disease prediction ensemble model is explained using SHapley Additive exPlanations (SHAP). Results The experimental results show that the AutoGluon-based ensemble model performs better than the individual baseline models in predicting CAD. It achieved an accuracy of 0.9167 and an AUC of 0.9562 in 4-fold cross-bagging. SHAP measures the importance of each feature to the prediction of the model and explains the prediction results of the model. Conclusion This study demonstrates the feasibility and efficacy of AutoML technology in cardiovascular medicine and highlights its potential in disease prediction. AutoML reduces the barriers to model building and significantly improves prediction accuracy. Additionally, the integration of SHAP enhances model transparency and explainability, which is critical to ensuring model credibility and widespread adoption in cardiovascular medicine.
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Affiliation(s)
- Jianghong Wang
- Faculty of Information Engineering and Automation, Center for Precision Medicine, Yan'an Hospital of Kunming City & Kunming University of Science and Technology, Kunming, China
| | - Qiang Xue
- Faculty of Information Engineering and Automation, Center for Precision Medicine, Yan'an Hospital of Kunming City & Kunming University of Science and Technology, Kunming, China
| | - Chris W J Zhang
- Department of Mechanical Engineering, University of Saskatchewan, Saskatoon, SK, Canada
| | | | - Zhihua Liu
- Faculty of Information Engineering and Automation, Center for Precision Medicine, Yan'an Hospital of Kunming City & Kunming University of Science and Technology, Kunming, China
- Department of Mechanical Engineering, University of Saskatchewan, Saskatoon, SK, Canada
- Bayer HealthCare & Dana-Farber Cancer Institute, Harvard University, Boston, MA, United States
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Alatrany AS, Khan W, Hussain A, Kolivand H, Al-Jumeily D. An explainable machine learning approach for Alzheimer's disease classification. Sci Rep 2024; 14:2637. [PMID: 38302557 PMCID: PMC10834965 DOI: 10.1038/s41598-024-51985-w] [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] [Accepted: 01/11/2024] [Indexed: 02/03/2024] Open
Abstract
The early diagnosis of Alzheimer's disease (AD) presents a significant challenge due to the subtle biomarker changes often overlooked. Machine learning (ML) models offer a promising tool for identifying individuals at risk of AD. However, current research tends to prioritize ML accuracy while neglecting the crucial aspect of model explainability. The diverse nature of AD data and the limited dataset size introduce additional challenges, primarily related to high dimensionality. In this study, we leveraged a dataset obtained from the National Alzheimer's Coordinating Center, comprising 169,408 records and 1024 features. After applying various steps to reduce the feature space. Notably, support vector machine (SVM) models trained on the selected features exhibited high performance when tested on an external dataset. SVM achieved a high F1 score of 98.9% for binary classification (distinguishing between NC and AD) and 90.7% for multiclass classification. Furthermore, SVM was able to predict AD progression over a 4-year period, with F1 scores reached 88% for binary task and 72.8% for multiclass task. To enhance model explainability, we employed two rule-extraction approaches: class rule mining and stable and interpretable rule set for classification model. These approaches generated human-understandable rules to assist domain experts in comprehending the key factors involved in AD development. We further validated these rules using SHAP and LIME models, underscoring the significance of factors such as MEMORY, JUDGMENT, COMMUN, and ORIENT in determining AD risk. Our experimental outcomes also shed light on the crucial role of the Clinical Dementia Rating tool in predicting AD.
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Affiliation(s)
- Abbas Saad Alatrany
- School of Computer Science and Mathematics, Liverpool John Moores University, Liverpool, UK.
- University of Information Technology and Communications, Baghdad, Iraq.
- Imam Ja'afar Al-Sadiq University, Baghdad, Iraq.
- NIHR Leicester Biomedical Research Centre, University of Leicester, Leicester, UK.
| | - Wasiq Khan
- School of Computer Science and Mathematics, Liverpool John Moores University, Liverpool, UK
| | - Abir Hussain
- School of Computer Science and Mathematics, Liverpool John Moores University, Liverpool, UK.
- Department of Electrical Engineering, University of Sharjah, Sharjah, United Arab Emirates.
| | - Hoshang Kolivand
- School of Computer Science and Mathematics, Liverpool John Moores University, Liverpool, UK
| | - Dhiya Al-Jumeily
- School of Computer Science and Mathematics, Liverpool John Moores University, Liverpool, UK
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Rhodes JS, Aumon A, Morin S, Girard M, Larochelle C, Brunet-Ratnasingham E, Pagliuzza A, Marchitto L, Zhang W, Cutler A, Grand'Maison F, Zhou A, Finzi A, Chomont N, Kaufmann DE, Zandee S, Prat A, Wolf G, Moon KR. Gaining Biological Insights through Supervised Data Visualization. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.11.22.568384. [PMID: 38293135 PMCID: PMC10827133 DOI: 10.1101/2023.11.22.568384] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2024]
Abstract
Dimensionality reduction-based data visualization is pivotal in comprehending complex biological data. The most common methods, such as PHATE, t-SNE, and UMAP, are unsupervised and therefore reflect the dominant structure in the data, which may be independent of expert-provided labels. Here we introduce a supervised data visualization method called RF-PHATE, which integrates expert knowledge for further exploration of the data. RF-PHATE leverages random forests to capture intricate featurelabel relationships. Extracting information from the forest, RF-PHATE generates low-dimensional visualizations that highlight relevant data relationships while disregarding extraneous features. This approach scales to large datasets and applies to classification and regression. We illustrate RF-PHATE's prowess through three case studies. In a multiple sclerosis study using longitudinal clinical and imaging data, RF-PHATE unveils a sub-group of patients with non-benign relapsingremitting Multiple Sclerosis, demonstrating its aptitude for time-series data. In the context of Raman spectral data, RF-PHATE effectively showcases the impact of antioxidants on diesel exhaust-exposed lung cells, highlighting its proficiency in noisy environments. Furthermore, RF-PHATE aligns established geometric structures with COVID-19 patient outcomes, enriching interpretability in a hierarchical manner. RF-PHATE bridges expert insights and visualizations, promising knowledge generation. Its adaptability, scalability, and noise tolerance underscore its potential for widespread adoption.
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Bai L, Wu Y, Li G, Zhang W, Zhang H, Su J. AI-enabled organoids: Construction, analysis, and application. Bioact Mater 2024; 31:525-548. [PMID: 37746662 PMCID: PMC10511344 DOI: 10.1016/j.bioactmat.2023.09.005] [Citation(s) in RCA: 23] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2023] [Revised: 09/09/2023] [Accepted: 09/09/2023] [Indexed: 09/26/2023] Open
Abstract
Organoids, miniature and simplified in vitro model systems that mimic the structure and function of organs, have attracted considerable interest due to their promising applications in disease modeling, drug screening, personalized medicine, and tissue engineering. Despite the substantial success in cultivating physiologically relevant organoids, challenges remain concerning the complexities of their assembly and the difficulties associated with data analysis. The advent of AI-Enabled Organoids, which interfaces with artificial intelligence (AI), holds the potential to revolutionize the field by offering novel insights and methodologies that can expedite the development and clinical application of organoids. This review succinctly delineates the fundamental concepts and mechanisms underlying AI-Enabled Organoids, summarizing the prospective applications on rapid screening of construction strategies, cost-effective extraction of multiscale image features, streamlined analysis of multi-omics data, and precise preclinical evaluation and application. We also explore the challenges and limitations of interfacing organoids with AI, and discuss the future direction of the field. Taken together, the AI-Enabled Organoids hold significant promise for advancing our understanding of organ development and disease progression, ultimately laying the groundwork for clinical application.
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Affiliation(s)
- Long Bai
- Department of Orthopedics, Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200092, China
- Organoid Research Center, Institute of Translational Medicine, Shanghai University, Shanghai, 200444, China
- National Center for Translational Medicine (Shanghai) SHU Branch, Shanghai University, Shanghai, 200444, China
- Wenzhou Institute of Shanghai University, Wenzhou, 325000, China
| | - Yan Wu
- Department of Orthopedics, Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200092, China
- Organoid Research Center, Institute of Translational Medicine, Shanghai University, Shanghai, 200444, China
- National Center for Translational Medicine (Shanghai) SHU Branch, Shanghai University, Shanghai, 200444, China
| | - Guangfeng Li
- Organoid Research Center, Institute of Translational Medicine, Shanghai University, Shanghai, 200444, China
- National Center for Translational Medicine (Shanghai) SHU Branch, Shanghai University, Shanghai, 200444, China
- Department of Orthopedics, Shanghai Zhongye Hospital, Shanghai, 201941, China
| | - Wencai Zhang
- Department of Orthopedics, First Affiliated Hospital, Jinan University, Guangzhou, 510632, China
| | - Hao Zhang
- Department of Orthopedics, Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200092, China
- Organoid Research Center, Institute of Translational Medicine, Shanghai University, Shanghai, 200444, China
- National Center for Translational Medicine (Shanghai) SHU Branch, Shanghai University, Shanghai, 200444, China
| | - Jiacan Su
- Department of Orthopedics, Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200092, China
- Organoid Research Center, Institute of Translational Medicine, Shanghai University, Shanghai, 200444, China
- National Center for Translational Medicine (Shanghai) SHU Branch, Shanghai University, Shanghai, 200444, China
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Li F, Bi Z, Xu H, Shi Y, Duan N, Li Z. Design and implementation of a smart Internet of Things chest pain center based on deep learning. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2023; 20:18987-19011. [PMID: 38052586 DOI: 10.3934/mbe.2023840] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/07/2023]
Abstract
The data input process for most chest pain centers is not intelligent, requiring a lot of staff to manually input patient information. This leads to problems such as long processing times, high potential for errors, an inability to access patient data in a timely manner and an increasing workload. To address the challenge, an Internet of Things (IoT)-driven chest pain center is designed, which crosses the sensing layer, network layer and application layer. The system enables the construction of intelligent chest pain management through a pre-hospital app, Ultra-Wideband (UWB) positioning, and in-hospital treatment. The pre-hospital app is provided to emergency medical services (EMS) centers, which allows them to record patient information in advance and keep it synchronized with the hospital's database, reducing the time needed for treatment. UWB positioning obtains the patient's hospital information through the zero-dimensional base station and the corresponding calculation engine, and in-hospital treatment involves automatic acquisition of patient information through web and mobile applications. The system also introduces the Bidirectional Long Short-Term Memory (BiLSTM)-Conditional Random Field (CRF)-based algorithm to train electronic medical record information for chest pain patients, extracting the patient's chest pain clinical symptoms. The resulting data are saved in the chest pain patient database and uploaded to the national chest pain center. The system has been used in Liaoning Provincial People's Hospital, and its subsequent assistance to doctors and nurses in collaborative treatment, data feedback and analysis is of great significance.
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Affiliation(s)
- Feng Li
- School of Information and Electronic Engineering, Zhejiang Gongshang University, Hangzhou 310018, China
- School of Computer Science and Engineering, Nanyang Technological University, 639798, Singapore
| | - Zhongao Bi
- School of Information and Electronic Engineering, Zhejiang Gongshang University, Hangzhou 310018, China
| | - Hongzeng Xu
- Department of Cardiology, The People's Hospital of Liaoning Province, Liaoning, Shenyang 110011, China
| | - Yunqi Shi
- Department of Cardiology, The People's Hospital of Liaoning Province, Liaoning, Shenyang 110011, China
| | - Na Duan
- Department of Cardiology, The People's Hospital of Liaoning Province, Liaoning, Shenyang 110011, China
| | - Zhaoyu Li
- Department of Cardiology, The Second Affiliated Hospital Zhejiang University School of Medicine, Hangzhou 310000, China
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Rao C, Huang Q, Chen L, Goh M, Hu Z. Forecasting the carbon emissions in Hubei Province under the background of carbon neutrality: a novel STIRPAT extended model with ridge regression and scenario analysis. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2023; 30:57460-57480. [PMID: 36964474 PMCID: PMC10038777 DOI: 10.1007/s11356-023-26599-w] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/24/2022] [Accepted: 03/17/2023] [Indexed: 05/07/2023]
Abstract
The impact of global greenhouse gas emissions is increasingly serious, and the development of green low-carbon circular economy has become an inevitable trend for the development of all countries in the world. To achieve emission peak and carbon neutrality is the primary goal of energy conservation and emission reduction. As the core province in central China, Hubei Province is under prominent pressure of carbon emission reduction. In this paper, the future development trend of carbon emissions is analyzed, and the emission peak value and carbon peak time in Hubei Province is predicted. Firstly, the generalized Divisia index method (GDIM) model is proposed to determine the main influencing factors of carbon emissions in Hubei Province. Secondly, based on the main influencing factors identified, a novel STIRPAT (Stochastic Impacts by Regression on Population, Affluence, and Technology) extended model with ridge regression is established to predict carbon emissions. Thirdly, the scenario analysis method is used to set the variables of the STIRPAT extended model and to predict the emission peak value and carbon peak time in Hubei Province. The results show that Hubei Province's carbon emissions peaked first in 2025, with a peak value of 361.81 million tons. Finally, according to the prediction results, the corresponding suggestions on carbon emission reduction are provided in three aspects of industrial structure, energy structure, and urbanization, so as to help government establish a green, low-carbon, and circular development economic system and achieve the industry's cleaner production and sustainable development of society.
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Affiliation(s)
- Congjun Rao
- School of Science, Wuhan University of Technology, Wuhan, 430070, People's Republic of China
| | - Qifan Huang
- School of Science, Wuhan University of Technology, Wuhan, 430070, People's Republic of China
| | - Lin Chen
- School of Management, Wuhan Institute of Technology, Wuhan, 430205, People's Republic of China
| | - Mark Goh
- NUS Business School & The Logistics Institute-Asia Pacific, National University of Singapore, Singapore, 119623, Singapore
| | - Zhuo Hu
- School of Automation, Wuhan University of Technology, Wuhan, 430070, People's Republic of China.
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Trigka M, Dritsas E. Long-Term Coronary Artery Disease Risk Prediction with Machine Learning Models. SENSORS (BASEL, SWITZERLAND) 2023; 23:1193. [PMID: 36772237 PMCID: PMC9920214 DOI: 10.3390/s23031193] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/28/2022] [Revised: 01/17/2023] [Accepted: 01/18/2023] [Indexed: 06/18/2023]
Abstract
The heart is the most vital organ of the human body; thus, its improper functioning has a significant impact on human life. Coronary artery disease (CAD) is a disease of the coronary arteries through which the heart is nourished and oxygenated. It is due to the formation of atherosclerotic plaques on the wall of the epicardial coronary arteries, resulting in the narrowing of their lumen and the obstruction of blood flow through them. Coronary artery disease can be delayed or even prevented with lifestyle changes and medical intervention. Long-term risk prediction of coronary artery disease will be the area of interest in this work. In this specific research paper, we experimented with various machine learning (ML) models after the use or non-use of the synthetic minority oversampling technique (SMOTE), evaluating and comparing them in terms of accuracy, precision, recall and an area under the curve (AUC). The results showed that the stacking ensemble model after the SMOTE with 10-fold cross-validation prevailed over the other models, achieving an accuracy of 90.9 %, a precision of 96.7%, a recall of 87.6% and an AUC equal to 96.1%.
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Affiliation(s)
- Maria Trigka
- Department of Computer Engineering and Informatics, University of Patras, 26504 Patras, Greece
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14
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Rao C, Gao M, Goh M, Xiao X. Green Supplier Selection Mechanism Based on Information Environment of Z-Numbers. Cognit Comput 2022. [DOI: 10.1007/s12559-022-10055-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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15
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Rao C, Liu Y, Goh M. Credit risk assessment mechanism of personal auto loan based on PSO-XGBoost Model. COMPLEX INTELL SYST 2022. [DOI: 10.1007/s40747-022-00854-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
AbstractAs online P2P loans in automotive financing grows, there is a need to manage and control the credit risk of the personal auto loans. In this paper, the personal auto loans data sets on the Kaggle platform are used on a machine learning based credit risk assessment mechanism for personal auto loans. An integrated Smote-Tomek Link algorithm is proposed to convert the data set into a balanced data set. Then, an improved Filter-Wrapper feature selection method is presented to select credit risk assessment indexes for the loans. Combining Particle Swarm Optimization (PSO) with the eXtreme Gradient Boosting (XGBoost) model, a PSO-XGBoost model is formed to assess the credit risk of the loans. The PSO-XGBoost model is compared against the XGBoost, Random Forest, and Logistic Regression models on the standard performance evaluation indexes of accuracy, precision, ROC curve, and AUC value. The PSO-XGBoost model is found to be superior on classification performance and classification effect.
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Peng JJ, Chen XG, Tian C, Zhang ZQ, Song HY, Dong F. Picture fuzzy large-scale group decision-making in a trust- relationship-based social network environment. Inf Sci (N Y) 2022. [DOI: 10.1016/j.ins.2022.07.019] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
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17
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A Boundedly Rational Decision-Making Model Based on Weakly Consistent Preference Relations. Symmetry (Basel) 2022. [DOI: 10.3390/sym14050918] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023] Open
Abstract
Completeness is one of the basic assumptions about the rational preference relation in classical decision theory. Strongly and weakly consistent preferences are presented by abandoning the completeness of the rational preference relation. Some expansion and contraction conditions are proposed and the relationships between these conditions of rationality are discussed. The relationships between the conditions of rationality and boundedly rational choice behavior based on strongly and weakly consistent preferences are analyzed and discussed. Furthermore, an example about the choices of chocolates with interval ordinal numbers is given to explain some of the main conclusions in this paper. The results can be used as references for the study of boundedly rational decisions.
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