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Horry MJ, Chakraborty S, Pradhan B, Paul M, Zhu J, Loh HW, Barua PD, Acharya UR. Development of Debiasing Technique for Lung Nodule Chest X-ray Datasets to Generalize Deep Learning Models. Sensors (Basel) 2023; 23:6585. [PMID: 37514877 PMCID: PMC10385599 DOI: 10.3390/s23146585] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/14/2023] [Revised: 07/16/2023] [Accepted: 07/20/2023] [Indexed: 07/30/2023]
Abstract
Screening programs for early lung cancer diagnosis are uncommon, primarily due to the challenge of reaching at-risk patients located in rural areas far from medical facilities. To overcome this obstacle, a comprehensive approach is needed that combines mobility, low cost, speed, accuracy, and privacy. One potential solution lies in combining the chest X-ray imaging mode with federated deep learning, ensuring that no single data source can bias the model adversely. This study presents a pre-processing pipeline designed to debias chest X-ray images, thereby enhancing internal classification and external generalization. The pipeline employs a pruning mechanism to train a deep learning model for nodule detection, utilizing the most informative images from a publicly available lung nodule X-ray dataset. Histogram equalization is used to remove systematic differences in image brightness and contrast. Model training is then performed using combinations of lung field segmentation, close cropping, and rib/bone suppression. The resulting deep learning models, generated through this pre-processing pipeline, demonstrate successful generalization on an independent lung nodule dataset. By eliminating confounding variables in chest X-ray images and suppressing signal noise from the bone structures, the proposed deep learning lung nodule detection algorithm achieves an external generalization accuracy of 89%. This approach paves the way for the development of a low-cost and accessible deep learning-based clinical system for lung cancer screening.
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Affiliation(s)
- Michael J Horry
- Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS), Faculty of Engineering and Information Technology, University of Technology Sydney, Ultimo, NSW 2007, Australia
- IBM Australia Limited, Sydney, NSW 2000, Australia
| | - Subrata Chakraborty
- Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS), Faculty of Engineering and Information Technology, University of Technology Sydney, Ultimo, NSW 2007, Australia
- Faculty of Science, Agriculture, Business and Law, University of New England, Armidale, NSW 2351, Australia
| | - Biswajeet Pradhan
- Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS), Faculty of Engineering and Information Technology, University of Technology Sydney, Ultimo, NSW 2007, Australia
- Earth Observation Center, Institute of Climate Change, Universiti Kebangsaan Malaysia, Bangi 43600, Malaysia
| | - Manoranjan Paul
- Machine Vision and Digital Health (MaViDH), School of Computing and Mathematics, Charles Sturt University, Bathurst, NSW 2795, Australia
| | - Jing Zhu
- Department of Radiology, Westmead Hospital, Westmead, NSW 2145, Australia
| | - Hui Wen Loh
- School of Science and Technology, Singapore University of Social Sciences, Singapore 599494, Singapore
| | - Prabal Datta Barua
- Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS), Faculty of Engineering and Information Technology, University of Technology Sydney, Ultimo, NSW 2007, Australia
- Faculty of Science, Agriculture, Business and Law, University of New England, Armidale, NSW 2351, Australia
- Cogninet Brain Team, Cogninet Australia, Sydney, NSW 2010, Australia
- School of Business (Information Systems), Faculty of Business, Education, Law & Arts, University of Southern Queensland, Toowoomba, QLD 4350, Australia
| | - U Rajendra Acharya
- School of Mathematics, Physics and Computing, University of Southern Queensland, Springfield, QLD 4300, Australia
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Cummings BC, Blackmer JM, Motyka JR, Farzaneh N, Cao L, Bisco EL, Glassbrook JD, Roebuck MD, Gillies CE, Admon AJ, Medlin RP, Singh K, Sjoding MW, Ward KR, Ansari S. External Validation and Comparison of a General Ward Deterioration Index Between Diversely Different Health Systems. Crit Care Med 2023; 51:775-786. [PMID: 36927631 PMCID: PMC10187626 DOI: 10.1097/ccm.0000000000005837] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/18/2023]
Abstract
OBJECTIVES Implementing a predictive analytic model in a new clinical environment is fraught with challenges. Dataset shifts such as differences in clinical practice, new data acquisition devices, or changes in the electronic health record (EHR) implementation mean that the input data seen by a model can differ significantly from the data it was trained on. Validating models at multiple institutions is therefore critical. Here, using retrospective data, we demonstrate how Predicting Intensive Care Transfers and other UnfoReseen Events (PICTURE), a deterioration index developed at a single academic medical center, generalizes to a second institution with significantly different patient population. DESIGN PICTURE is a deterioration index designed for the general ward, which uses structured EHR data such as laboratory values and vital signs. SETTING The general wards of two large hospitals, one an academic medical center and the other a community hospital. SUBJECTS The model has previously been trained and validated on a cohort of 165,018 general ward encounters from a large academic medical center. Here, we apply this model to 11,083 encounters from a separate community hospital. INTERVENTIONS None. MEASUREMENTS AND MAIN RESULTS The hospitals were found to have significant differences in missingness rates (> 5% difference in 9/52 features), deterioration rate (4.5% vs 2.5%), and racial makeup (20% non-White vs 49% non-White). Despite these differences, PICTURE's performance was consistent (area under the receiver operating characteristic curve [AUROC], 0.870; 95% CI, 0.861-0.878), area under the precision-recall curve (AUPRC, 0.298; 95% CI, 0.275-0.320) at the first hospital; AUROC 0.875 (0.851-0.902), AUPRC 0.339 (0.281-0.398) at the second. AUPRC was standardized to a 2.5% event rate. PICTURE also outperformed both the Epic Deterioration Index and the National Early Warning Score at both institutions. CONCLUSIONS Important differences were observed between the two institutions, including data availability and demographic makeup. PICTURE was able to identify general ward patients at risk of deterioration at both hospitals with consistent performance (AUROC and AUPRC) and compared favorably to existing metrics.
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Affiliation(s)
- Brandon C Cummings
- The Max Harry Weil Institute of Critical Care Research & Innovation, University of Michigan, Ann Arbor, MI
- Department of Emergency Medicine, University of Michigan Medical School, Ann Arbor, MI
| | - Joseph M Blackmer
- The Max Harry Weil Institute of Critical Care Research & Innovation, University of Michigan, Ann Arbor, MI
- Department of Emergency Medicine, University of Michigan Medical School, Ann Arbor, MI
| | - Jonathan R Motyka
- The Max Harry Weil Institute of Critical Care Research & Innovation, University of Michigan, Ann Arbor, MI
- Department of Emergency Medicine, University of Michigan Medical School, Ann Arbor, MI
| | - Negar Farzaneh
- The Max Harry Weil Institute of Critical Care Research & Innovation, University of Michigan, Ann Arbor, MI
- Department of Emergency Medicine, University of Michigan Medical School, Ann Arbor, MI
| | - Loc Cao
- The Max Harry Weil Institute of Critical Care Research & Innovation, University of Michigan, Ann Arbor, MI
- Department of Emergency Medicine, University of Michigan Medical School, Ann Arbor, MI
| | - Erin L Bisco
- The Max Harry Weil Institute of Critical Care Research & Innovation, University of Michigan, Ann Arbor, MI
- Department of Emergency Medicine, University of Michigan Medical School, Ann Arbor, MI
| | | | - Michael D Roebuck
- Department of Emergency Medicine, University of Michigan Medical School, Ann Arbor, MI
- Department of Emergency Medicine, Hurley Medical Center, Flint, MI
| | - Christopher E Gillies
- The Max Harry Weil Institute of Critical Care Research & Innovation, University of Michigan, Ann Arbor, MI
- Department of Emergency Medicine, University of Michigan Medical School, Ann Arbor, MI
| | - Andrew J Admon
- The Max Harry Weil Institute of Critical Care Research & Innovation, University of Michigan, Ann Arbor, MI
- Department of Internal Medicine, University of Michigan Medical School, Ann Arbor, MI
- Medicine Service, LTC Charles S. Kettles VA Medical Center, Ann Arbor, MI
| | - Richard P Medlin
- The Max Harry Weil Institute of Critical Care Research & Innovation, University of Michigan, Ann Arbor, MI
- Department of Emergency Medicine, University of Michigan Medical School, Ann Arbor, MI
| | - Karandeep Singh
- Department of Internal Medicine, University of Michigan Medical School, Ann Arbor, MI
- Department of Learning Health Sciences, University of Michigan Medical School, Ann Arbor, MI
- Precision Health, University of Michigan, Ann Arbor, MI
| | - Michael W Sjoding
- The Max Harry Weil Institute of Critical Care Research & Innovation, University of Michigan, Ann Arbor, MI
- Department of Internal Medicine, University of Michigan Medical School, Ann Arbor, MI
- Precision Health, University of Michigan, Ann Arbor, MI
| | - Kevin R Ward
- The Max Harry Weil Institute of Critical Care Research & Innovation, University of Michigan, Ann Arbor, MI
- Department of Emergency Medicine, University of Michigan Medical School, Ann Arbor, MI
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI
| | - Sardar Ansari
- The Max Harry Weil Institute of Critical Care Research & Innovation, University of Michigan, Ann Arbor, MI
- Department of Emergency Medicine, University of Michigan Medical School, Ann Arbor, MI
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Nazir S, Kaleem M. Federated Learning for Medical Image Analysis with Deep Neural Networks. Diagnostics (Basel) 2023; 13:diagnostics13091532. [PMID: 37174925 PMCID: PMC10177193 DOI: 10.3390/diagnostics13091532] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2023] [Revised: 04/14/2023] [Accepted: 04/21/2023] [Indexed: 05/15/2023] Open
Abstract
Medical image analysis using deep neural networks (DNN) has demonstrated state-of-the-art performance in image classification and segmentation tasks, aiding disease diagnosis. The accuracy of the DNN is largely governed by the quality and quantity of the data used to train the model. However, for the medical images, the critical security and privacy concerns regarding sharing of local medical data across medical establishments precludes exploiting the full DNN potential for clinical diagnosis. The federated learning (FL) approach enables the use of local model's parameters to train a global model, while ensuring data privacy and security. In this paper, we review the federated learning applications in medical image analysis with DNNs, highlight the security concerns, cover some efforts to improve FL model performance, and describe the challenges and future research directions.
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Affiliation(s)
- Sajid Nazir
- Department of Computing, Glasgow Caledonian University, Glasgow G4 0BA, UK
| | - Mohammad Kaleem
- Department of Electrical and Computer Engineering, COMSATS University Islamabad, Islamabad 45550, Pakistan
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Meng D, Wang S, Wong PCM, Feng G. Generalizable predictive modeling of semantic processing ability from functional brain connectivity. Hum Brain Mapp 2022; 43:4274-4292. [PMID: 35611721 PMCID: PMC9435002 DOI: 10.1002/hbm.25953] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2021] [Revised: 03/11/2022] [Accepted: 05/06/2022] [Indexed: 11/08/2022] Open
Abstract
Semantic processing (SP) is one of the critical abilities of humans for representing and manipulating conceptual and meaningful information. Neuroimaging studies of SP typically collapse data from many subjects, but its neural organization and behavioral performance vary between individuals. It is not yet understood whether and how the individual variabilities in neural network organizations contribute to the individual differences in SP behaviors. We aim to identify the neural signatures underlying SP variabilities by analyzing functional connectivity (FC) patterns based on a large‐sample Human Connectome Project (HCP) dataset and rigorous predictive modeling. We used a two‐stage predictive modeling approach to build an internally cross‐validated model and to test the model's generalizability with unseen data from different HCP samples and other out‐of‐sample datasets. FC patterns within a putative semantic brain network were significantly predictive of individual SP scores summarized from five SP‐related behavioral tests. This cross‐validated model can be used to predict unseen HCP data. The model generalizability was enhanced in the language task compared with other tasks used during scanning and was better for females than males. The model constructed from the HCP dataset can be partially generalized to two independent cohorts that participated in different semantic tasks. FCs connecting to the Perisylvian language network show the most reliable contributions to predictive modeling and the out‐of‐sample generalization. These findings contribute to our understanding of the neural sources of individual differences in SP, which potentially lay the foundation for personalized education for healthy individuals and intervention for SP and language deficits patients.
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Affiliation(s)
- Danting Meng
- Philosophy and Social Science Laboratory of Reading and Development in Children and Adolescents (South China Normal University), Ministry of Education, Guangzhou, China.,Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou, China
| | - Suiping Wang
- Philosophy and Social Science Laboratory of Reading and Development in Children and Adolescents (South China Normal University), Ministry of Education, Guangzhou, China
| | - Patrick C M Wong
- Department of Linguistics and Modern Languages, The Chinese University of Hong Kong, Hong Kong SAR, China.,Brain and Mind Institute, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Gangyi Feng
- Department of Linguistics and Modern Languages, The Chinese University of Hong Kong, Hong Kong SAR, China.,Brain and Mind Institute, The Chinese University of Hong Kong, Hong Kong SAR, China
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5
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Chen C, Bai W, Davies RH, Bhuva AN, Manisty CH, Augusto JB, Moon JC, Aung N, Lee AM, Sanghvi MM, Fung K, Paiva JM, Petersen SE, Lukaschuk E, Piechnik SK, Neubauer S, Rueckert D. Improving the Generalizability of Convolutional Neural Network-Based Segmentation on CMR Images. Front Cardiovasc Med 2020; 7:105. [PMID: 32714943 PMCID: PMC7344224 DOI: 10.3389/fcvm.2020.00105] [Citation(s) in RCA: 42] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2019] [Accepted: 05/20/2020] [Indexed: 11/22/2022] Open
Abstract
Background: Convolutional neural network (CNN) based segmentation methods provide an efficient and automated way for clinicians to assess the structure and function of the heart in cardiac MR images. While CNNs can generally perform the segmentation tasks with high accuracy when training and test images come from the same domain (e.g., same scanner or site), their performance often degrades dramatically on images from different scanners or clinical sites. Methods: We propose a simple yet effective way for improving the network generalization ability by carefully designing data normalization and augmentation strategies to accommodate common scenarios in multi-site, multi-scanner clinical imaging data sets. We demonstrate that a neural network trained on a single-site single-scanner dataset from the UK Biobank can be successfully applied to segmenting cardiac MR images across different sites and different scanners without substantial loss of accuracy. Specifically, the method was trained on a large set of 3,975 subjects from the UK Biobank. It was then directly tested on 600 different subjects from the UK Biobank for intra-domain testing and two other sets for cross-domain testing: the ACDC dataset (100 subjects, 1 site, 2 scanners) and the BSCMR-AS dataset (599 subjects, 6 sites, 9 scanners). Results: The proposed method produces promising segmentation results on the UK Biobank test set which are comparable to previously reported values in the literature, while also performing well on cross-domain test sets, achieving a mean Dice metric of 0.90 for the left ventricle, 0.81 for the myocardium, and 0.82 for the right ventricle on the ACDC dataset; and 0.89 for the left ventricle, 0.83 for the myocardium on the BSCMR-AS dataset. Conclusions: The proposed method offers a potential solution to improve CNN-based model generalizability for the cross-scanner and cross-site cardiac MR image segmentation task.
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Affiliation(s)
- Chen Chen
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom
| | - Wenjia Bai
- Data Science Institute, Imperial College London, London, United Kingdom.,Department of Brain Sciences, Imperial College London, London, United Kingdom
| | - Rhodri H Davies
- Institute of Cardiovascular Science, University College London, London, United Kingdom.,Department of Cardiovascular Imaging, Barts Heart Centre, St Bartholomew's Hospital, London, United Kingdom
| | - Anish N Bhuva
- Institute of Cardiovascular Science, University College London, London, United Kingdom.,Department of Cardiovascular Imaging, Barts Heart Centre, St Bartholomew's Hospital, London, United Kingdom
| | - Charlotte H Manisty
- Institute of Cardiovascular Science, University College London, London, United Kingdom.,Department of Cardiovascular Imaging, Barts Heart Centre, St Bartholomew's Hospital, London, United Kingdom
| | - Joao B Augusto
- Institute of Cardiovascular Science, University College London, London, United Kingdom.,Department of Cardiovascular Imaging, Barts Heart Centre, St Bartholomew's Hospital, London, United Kingdom
| | - James C Moon
- Institute of Cardiovascular Science, University College London, London, United Kingdom.,Department of Cardiovascular Imaging, Barts Heart Centre, St Bartholomew's Hospital, London, United Kingdom
| | - Nay Aung
- Department of Cardiovascular Imaging, Barts Heart Centre, St Bartholomew's Hospital, London, United Kingdom.,NIHR Biomedical Research Centre at Barts, Queen Mary University of London, London, United Kingdom
| | - Aaron M Lee
- Department of Cardiovascular Imaging, Barts Heart Centre, St Bartholomew's Hospital, London, United Kingdom.,NIHR Biomedical Research Centre at Barts, Queen Mary University of London, London, United Kingdom
| | - Mihir M Sanghvi
- Department of Cardiovascular Imaging, Barts Heart Centre, St Bartholomew's Hospital, London, United Kingdom.,NIHR Biomedical Research Centre at Barts, Queen Mary University of London, London, United Kingdom
| | - Kenneth Fung
- Department of Cardiovascular Imaging, Barts Heart Centre, St Bartholomew's Hospital, London, United Kingdom.,NIHR Biomedical Research Centre at Barts, Queen Mary University of London, London, United Kingdom
| | - Jose Miguel Paiva
- Department of Cardiovascular Imaging, Barts Heart Centre, St Bartholomew's Hospital, London, United Kingdom.,NIHR Biomedical Research Centre at Barts, Queen Mary University of London, London, United Kingdom
| | - Steffen E Petersen
- Department of Cardiovascular Imaging, Barts Heart Centre, St Bartholomew's Hospital, London, United Kingdom.,NIHR Biomedical Research Centre at Barts, Queen Mary University of London, London, United Kingdom
| | - Elena Lukaschuk
- NIHR BRC Oxford, Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, London, United Kingdom
| | - Stefan K Piechnik
- NIHR BRC Oxford, Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, London, United Kingdom
| | - Stefan Neubauer
- NIHR BRC Oxford, Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, London, United Kingdom
| | - Daniel Rueckert
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom
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Li H, Gong XJ, Yu H, Zhou C. Deep Neural Network Based Predictions of Protein Interactions Using Primary Sequences. Molecules 2018; 23:E1923. [PMID: 30071670 DOI: 10.3390/molecules23081923] [Citation(s) in RCA: 57] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2018] [Revised: 07/16/2018] [Accepted: 07/28/2018] [Indexed: 01/01/2023] Open
Abstract
Machine learning based predictions of protein–protein interactions (PPIs) could provide valuable insights into protein functions, disease occurrence, and therapy design on a large scale. The intensive feature engineering in most of these methods makes the prediction task more tedious and trivial. The emerging deep learning technology enabling automatic feature engineering is gaining great success in various fields. However, the over-fitting and generalization of its models are not yet well investigated in most scenarios. Here, we present a deep neural network framework (DNN-PPI) for predicting PPIs using features learned automatically only from protein primary sequences. Within the framework, the sequences of two interacting proteins are sequentially fed into the encoding, embedding, convolution neural network (CNN), and long short-term memory (LSTM) neural network layers. Then, a concatenated vector of the two outputs from the previous layer is wired as the input of the fully connected neural network. Finally, the Adam optimizer is applied to learn the network weights in a back-propagation fashion. The different types of features, including semantic associations between amino acids, position-related sequence segments (motif), and their long- and short-term dependencies, are captured in the embedding, CNN and LSTM layers, respectively. When the model was trained on Pan’s human PPI dataset, it achieved a prediction accuracy of 98.78% at the Matthew’s correlation coefficient (MCC) of 97.57%. The prediction accuracies for six external datasets ranged from 92.80% to 97.89%, making them superior to those achieved with previous methods. When performed on Escherichia coli, Drosophila, and Caenorhabditis elegans datasets, DNN-PPI obtained prediction accuracies of 95.949%, 98.389%, and 98.669%, respectively. The performances in cross-species testing among the four species above coincided in their evolutionary distances. However, when testing Mus Musculus using the models from those species, they all obtained prediction accuracies of over 92.43%, which is difficult to achieve and worthy of note for further study. These results suggest that DNN-PPI has remarkable generalization and is a promising tool for identifying protein interactions.
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Klebanov BB, Priniski S, Burstein J, Gyawali B, Harackiewicz J, Thoman D. Utility-Value Score: A Case Study in System Generalization for Writing Analytics. J Writ Anal 2018; 2:314-328. [PMID: 31565684 PMCID: PMC6764525] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Collection and analysis of students' writing samples on a large scale is a part of the research agenda of the emerging writing analytics community that promises to deliver an unprecedented insight into characteristics of student writing. Yet with a large scale often comes variability of contexts in which the samples were produced-different institutions, different purposes of writing, different author demographics, to name just a few possible dimensions of variation. What are the implications of such variation for the ability of automated methods to create indices/features based on the writing samples that would be valid and meaningful? This paper presents a case study in system generalization. Building on a system developed to assess the expression of utility value (a social-psychology-based construct) in essays written by first-year biology students at one postsecondary institution, we vary data parameters and observe system performance. From the point of view of social psychology, all these variants represent the same underlying construct (i.e., utility value), and it is thus very tempting to think that an automatically produced utility-value score could provide a meaningful analytic, consistently, on a large collection of essays. However, findings from this research show that there are challenges: Some variations are easier to deal with than others, and some components of the automated system generalize better than others. The findings are then discussed both in the context of the case study and more generally.
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Abstract
BACKGROUND The complexity of genome-scale metabolic models makes them quite difficult for human users to read, since they contain thousands of reactions that must be included for accurate computer simulation. Interestingly, hidden similarities between groups of reactions can be discovered, and generalized to reveal higher-level patterns. RESULTS The web-based navigation system Mimoza allows a human expert to explore metabolic network models in a semantically zoomable manner: The most general view represents the compartments of the model; the next view shows the generalized versions of reactions and metabolites in each compartment; and the most detailed view represents the initial network with the generalization-based layout (where similar metabolites and reactions are placed next to each other). It allows a human expert to grasp the general structure of the network and analyze it in a top-down manner CONCLUSIONS Mimoza can be installed standalone, or used on-line at http://mimoza.bordeaux.inria.fr/ , or installed in a Galaxy server for use in workflows. Mimoza views can be embedded in web pages, or downloaded as COMBINE archives.
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Affiliation(s)
- Anna Zhukova
- Inria/Université Bordeaux/CNRS joint project-team MAGNOME, 351, cours de la Libération, Talence, F-33405, France.
| | - David J Sherman
- Inria/Université Bordeaux/CNRS joint project-team MAGNOME, 351, cours de la Libération, Talence, F-33405, France.
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