{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T14:08:36Z","timestamp":1790863716954,"version":"4.1.0"},"update-to":[{"DOI":"10.1371\/journal.pcbi.1013436","type":"new_version","label":"New version","source":"publisher","updated":{"date-parts":[[2025,10,14]],"date-time":"2025-10-14T00:00:00Z","timestamp":1760400000000}}],"reference-count":69,"publisher":"Public Library of Science (PLoS)","issue":"10","license":[{"start":{"date-parts":[[2025,10,3]],"date-time":"2025-10-03T00:00:00Z","timestamp":1759449600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["EEC-1647837"],"award-info":[{"award-number":["EEC-1647837"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000968","name":"American Heart Association","doi-asserted-by":"publisher","award":["856354"],"award-info":[{"award-number":["856354"]}],"id":[{"id":"10.13039\/100000968","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["www.ploscompbiol.org"],"crossmark-restriction":false},"short-container-title":["PLoS Comput Biol"],"abstract":"<jats:p>In cardiac cells, structural organization is an important indicator of cell maturity and healthy function. Healthy and mature cardiomyocytes exhibit a highly organized structure, characterized by well-aligned almost crystalline morphology with densely packed and organized sarcomeres. Immature and\/or diseased cardiomyocytes typically lack this highly organized structure. Critically, human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) offer a valuable model for studying human cardiac cells in a controlled, patient-specific, and minimally invasive manner. However, these cells often exhibit a disorganized and difficult to quantify structure both in their immature form and as disease models. In this work, we extend the SarcGraph computational framework\u2014designed specifically to assess the structural and functional behavior of hiPSC-CMs\u2014to better accommodate the structural features of immature cells. There are two key enhancements: (1) incorporating a deep learning-based z-disc classifier, and (2) introducing a novel ensemble graph-scoring approach. These modification significantly reduced false positive sarcomere detections, particularly in immature cells, and improved the detection of longer myofibrils in mature samples. With this enhanced framework, we analyze an open-source dataset published by the Allen Institute for Cell Science, where, for the first time, we are able to extract key structural features from these data using information from each individually detected sarcomere. Not only are we able to use these structural features to predict expert scores, but we are also able to use these structural features to identify bias in expert scoring and offer an alternative unsupervised learning approach based on explainable clustering. These results demonstrate the efficacy of our modified SarcGraph algorithm in extracting biologically meaningful structural features, enabling a deeper understanding of hiPSC-CM structural integrity. By making our code and tools open-source, we aim to empower the broader cardiac research community and foster further development of computational tools for cardiac tissue analysis.<\/jats:p>","DOI":"10.1371\/journal.pcbi.1013436","type":"journal-article","created":{"date-parts":[[2025,10,3]],"date-time":"2025-10-03T17:39:14Z","timestamp":1759513154000},"page":"e1013436","update-policy":"https:\/\/doi.org\/10.1371\/journal.pcbi.corrections_policy","source":"Crossref","is-referenced-by-count":6,"title":["Quantifying HiPSC-CM structural organization at scale with deep learning-enhanced SarcGraph"],"prefix":"10.1371","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9879-044X","authenticated-orcid":true,"given":"Saeed","family":"Mohammadzadeh","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8099-3468","authenticated-orcid":true,"given":"Emma","family":"Lejeune","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2025,10,3]]},"reference":[{"key":"pcbi.1013436.ref001","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1016\/j.actbio.2018.12.053","article-title":"Multiscale characterization of heart failure","volume":"86","author":"F Sahli Costabal","year":"2019","journal-title":"Acta Biomater"},{"key":"pcbi.1013436.ref002","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1016\/j.actbio.2023.10.025","article-title":"A data-driven computational model for engineered cardiac microtissues","volume":"172","author":"J Jilberto","year":"2023","journal-title":"Acta Biomater"},{"key":"pcbi.1013436.ref003","doi-asserted-by":"crossref","first-page":"154","DOI":"10.1016\/j.actbio.2020.12.006","article-title":"Right ventricular myocardial mechanics: Multi-modal deformation, microstructure, modeling, and comparison to the left ventricle","volume":"123","author":"S Kakaletsis","year":"2021","journal-title":"Acta Biomater"},{"issue":"8","key":"pcbi.1013436.ref004","doi-asserted-by":"crossref","DOI":"10.1161\/CIR.0000000000001209","article-title":"2024 Heart Disease and Stroke Statistics: A Report of US and Global Data From the American Heart Association","volume":"149","author":"SS Martin","year":"2024","journal-title":"Circulation"},{"issue":"2","key":"pcbi.1013436.ref005","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1038\/nrd.2016.245","article-title":"Induced pluripotent stem cell technology: a decade of progress","volume":"16","author":"Y Shi","year":"2017","journal-title":"Nat Rev Drug Discov"},{"issue":"1","key":"pcbi.1013436.ref006","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1186\/2050-490X-2-4","article-title":"Human induced pluripotent stem cell for modeling cardiovascular diseases","volume":"2","author":"P Liang","year":"2014","journal-title":"Regen Med Res"},{"issue":"9","key":"pcbi.1013436.ref007","doi-asserted-by":"crossref","first-page":"1146","DOI":"10.1161\/CIRCRESAHA.111.240374","article-title":"Derivation of human induced pluripotent stem cells for cardiovascular disease modeling","volume":"108","author":"K Narsinh","year":"2011","journal-title":"Circ Res"},{"issue":"14","key":"pcbi.1013436.ref008","doi-asserted-by":"crossref","first-page":"1828","DOI":"10.1093\/cvr\/cvy208","article-title":"Modelling inherited cardiac disease using human induced pluripotent stem cell-derived cardiomyocytes: progress, pitfalls, and potential","volume":"114","author":"A van Mil","year":"2018","journal-title":"Cardiovasc Res"},{"issue":"16","key":"pcbi.1013436.ref009","doi-asserted-by":"crossref","first-page":"1677","DOI":"10.1161\/CIRCULATIONAHA.113.001883","article-title":"Drug screening using a library of human induced pluripotent stem cell-derived cardiomyocytes reveals disease-specific patterns of cardiotoxicity","volume":"127","author":"P Liang","year":"2013","journal-title":"Circulation"},{"issue":"3","key":"pcbi.1013436.ref010","doi-asserted-by":"crossref","first-page":"311","DOI":"10.1016\/j.stem.2016.07.006","article-title":"Transcriptome profiling of patient-specific human iPSC-cardiomyocytes predicts individual drug safety and efficacy responses in vitro","volume":"19","author":"E Matsa","year":"2016","journal-title":"Cell stem cell"},{"key":"pcbi.1013436.ref011","doi-asserted-by":"crossref","first-page":"80","DOI":"10.3389\/fphys.2018.00080","article-title":"Structural Immaturity of Human iPSC-Derived Cardiomyocytes: In Silico Investigation of Effects on Function and Disease Modeling","volume":"9","author":"JT Koivum\u00e4ki","year":"2018","journal-title":"Front Physiol"},{"issue":"9","key":"pcbi.1013436.ref012","doi-asserted-by":"crossref","first-page":"3404","DOI":"10.3390\/ijms21093404","article-title":"Modeling Cardiovascular Diseases with hiPSC-Derived Cardiomyocytes in 2D and 3D Cultures","volume":"21","author":"C Sacchetto","year":"2020","journal-title":"Int J Mol Sci"},{"issue":"26","key":"pcbi.1013436.ref013","doi-asserted-by":"crossref","DOI":"10.1002\/adma.202200217","article-title":"Programming Cellular Alignment in Engineered Cardiac Tissue via Bioprinting Anisotropic Organ Building Blocks","volume":"34","author":"JH Ahrens","year":"2022","journal-title":"Adv Mater"},{"issue":"1","key":"pcbi.1013436.ref014","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1038\/s41592-024-02480-7","article-title":"Induced pluripotent stem cell-derived cardiomyocyte in vitro models: benchmarking progress and ongoing challenges","volume":"22","author":"JK Ewoldt","year":"2025","journal-title":"Nat Methods"},{"issue":"1","key":"pcbi.1013436.ref015","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1186\/s12859-020-3466-1","article-title":"Automated image analysis system for studying cardiotoxicity in human pluripotent stem cell-Derived cardiomyocytes","volume":"21","author":"L Cao","year":"2020","journal-title":"BMC Bioinformatics"},{"issue":"10","key":"pcbi.1013436.ref016","doi-asserted-by":"crossref","first-page":"1572","DOI":"10.1161\/CIRCRESAHA.116.310363","article-title":"Multi-Imaging Method to Assay the Contractile Mechanical Output of Micropatterned Human iPSC-Derived Cardiac Myocytes","volume":"120","author":"AJS Ribeiro","year":"2017","journal-title":"Circ Res"},{"issue":"1","key":"pcbi.1013436.ref017","doi-asserted-by":"crossref","first-page":"1258","DOI":"10.1038\/s41598-018-19539-z","article-title":"High content analysis identifies unique morphological features of reprogrammed cardiomyocytes","volume":"8","author":"MD Sutcliffe","year":"2018","journal-title":"Sci Rep"},{"issue":"1","key":"pcbi.1013436.ref018","doi-asserted-by":"crossref","first-page":"254","DOI":"10.1093\/micmic\/ozac016","article-title":"Automated Sarcomere Structure Analysis for Studying Cardiotoxicity in Human Pluripotent Stem Cell-Derived Cardiomyocytes","volume":"29","author":"L Cao","year":"2022","journal-title":"Microscopy and Microanalysis"},{"issue":"3","key":"pcbi.1013436.ref019","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pcbi.1007676","article-title":"Striated myocyte structural integrity: Automated analysis of sarcomeric z-discs","volume":"16","author":"TA Morris","year":"2020","journal-title":"PLoS Comput Biol"},{"issue":"2","key":"pcbi.1013436.ref020","doi-asserted-by":"crossref","DOI":"10.1152\/ajpcell.00094.2016","article-title":"SarcOptiM for ImageJ: high-frequency online sarcomere length computing on stimulated cardiomyocytes","volume":"311","author":"C Pasqualin","year":"2016","journal-title":"Am J Physiol Cell Physiol"},{"issue":"5","key":"pcbi.1013436.ref021","doi-asserted-by":"crossref","first-page":"637","DOI":"10.1007\/s12551-021-00840-7","article-title":"Cardiac sarcomere mechanics in health and disease","volume":"13","author":"C Crocini","year":"2021","journal-title":"Biophys Rev"},{"issue":"4","key":"pcbi.1013436.ref022","doi-asserted-by":"crossref","first-page":"446","DOI":"10.1161\/hc0402.102990","article-title":"Sarcomere protein gene mutations in hypertrophic cardiomyopathy of the elderly","volume":"105","author":"H Niimura","year":"2002","journal-title":"Circulation"},{"issue":"8","key":"pcbi.1013436.ref023","doi-asserted-by":"crossref","first-page":"641","DOI":"10.1002\/cm.20290","article-title":"Sarcomere alignment is regulated by myocyte shape","volume":"65","author":"M-A Bray","year":"2008","journal-title":"Cell Motil Cytoskeleton"},{"issue":"5","key":"pcbi.1013436.ref024","article-title":"Measurement and analysis of sarcomere length in rat cardiomyocytes in situ and in vitro","volume":"298","author":"G Bub","year":"2010","journal-title":"Am J Physiol Heart Circ Physiol"},{"issue":"5","key":"pcbi.1013436.ref025","doi-asserted-by":"crossref","first-page":"786","DOI":"10.1109\/PROC.1979.11328","article-title":"Statistical and structural approaches to texture","volume":"67","author":"RM Haralick","year":"1979","journal-title":"Proc IEEE"},{"issue":"7","key":"pcbi.1013436.ref026","doi-asserted-by":"crossref","DOI":"10.1002\/cpz1.462","article-title":"Software Tool for Automatic Quantification of Sarcomere Length and Organization in Fixed and Live 2D and 3D Muscle Cell Cultures In Vitro","volume":"2","author":"JM Stein","year":"2022","journal-title":"Curr Protoc"},{"issue":"3","key":"pcbi.1013436.ref027","doi-asserted-by":"crossref","first-page":"340","DOI":"10.1016\/j.stemcr.2015.01.020","article-title":"Structural phenotyping of stem cell-derived cardiomyocytes","volume":"4","author":"FS Pasqualini","year":"2015","journal-title":"Stem Cell Reports"},{"issue":"1","key":"pcbi.1013436.ref028","doi-asserted-by":"crossref","first-page":"5427","DOI":"10.1038\/s41467-024-49755-3","article-title":"Tracking single hiPSC-derived cardiomyocyte contractile function using CONTRAX an efficient pipeline for traction force measurement","volume":"15","author":"G Pardon","year":"2024","journal-title":"Nat Commun"},{"issue":"8","key":"pcbi.1013436.ref029","doi-asserted-by":"crossref","first-page":"1172","DOI":"10.1161\/CIRCRESAHA.118.314505","article-title":"SarcTrack","volume":"124","author":"CN Toepfer","year":"2019","journal-title":"Circ Res"},{"issue":"10","key":"pcbi.1013436.ref030","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pcbi.1009443","article-title":"Sarc-Graph: Automated segmentation, tracking, and analysis of sarcomeres in hiPSC-derived cardiomyocytes","volume":"17","author":"B Zhao","year":"2021","journal-title":"PLoS Comput Biol"},{"issue":"85","key":"pcbi.1013436.ref031","doi-asserted-by":"crossref","first-page":"5322","DOI":"10.21105\/joss.05322","article-title":"SarcGraph: A Python package for analyzing the contractile behavior of pluripotent stem cell-derived cardiomyocytes","volume":"8","author":"S Mohammadzadeh","year":"2023","journal-title":"JOSS"},{"issue":"6","key":"pcbi.1013436.ref032","article-title":"Cell states beyond transcriptomics: Integrating structural organization and gene expression in hiPSC-derived cardiomyocytes","volume":"12","author":"KA Gerbin","year":"2021","journal-title":"Cell Syst"},{"key":"pcbi.1013436.ref033","doi-asserted-by":"crossref","unstructured":"Gerbin KA, Grancharova T, Donovan-Maiye RM, Hendershott MC, Anderson HG, Brown JM, et al. Data set for Cell states beyond transcriptomics: integrating structural organization and gene expression in hiPSC-derived cardiomyocytes. 2021. https:\/\/open.quiltdata.com\/b\/allencell\/packages\/aics\/integrated_transcriptomics_structural_organization_hipsc_cm.","DOI":"10.1016\/j.cels.2021.05.001"},{"issue":"8","key":"pcbi.1013436.ref034","doi-asserted-by":"crossref","first-page":"995","DOI":"10.1038\/s41592-022-01541-z","article-title":"Self-supervised deep learning encodes high-resolution features of protein subcellular localization","volume":"19","author":"H Kobayashi","year":"2022","journal-title":"Nat Methods"},{"key":"pcbi.1013436.ref035","doi-asserted-by":"crossref","unstructured":"Le H, Dang K, Lai T, Nguyen N, Tran M, Pham H. SarcNet: A Novel AI-based Framework to Automatically Analyze and Score Sarcomere Organizations in Fluorescently Tagged hiPSC-CMs. arXiv preprint arXiv:2405.17926. 2024.","DOI":"10.1109\/ISBI60581.2025.10981011"},{"issue":"10","key":"pcbi.1013436.ref036","doi-asserted-by":"crossref","DOI":"10.1186\/gb-2006-7-10-r100","article-title":"CellProfiler: image analysis software for identifying and quantifying cell phenotypes","volume":"7","author":"AE Carpenter","year":"2006","journal-title":"Genome Biol"},{"key":"pcbi.1013436.ref037","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1007\/s10974-009-9189-6","article-title":"The vertebrate muscle Z-disc: sarcomere anchor for structure and signalling","volume":"30","author":"PK Luther","year":"2009","journal-title":"J Muscle Res Cell Motil"},{"issue":"4","key":"pcbi.1013436.ref038","doi-asserted-by":"crossref","first-page":"399","DOI":"10.1111\/j.1745-3984.2011.00152.x","article-title":"Rater Effects on Essay Scoring: A Multilevel Analysis of Severity Drift, Central Tendency, and Rater Experience","volume":"48","author":"G Leckie","year":"2011","journal-title":"Journal of Educational Measurement"},{"issue":"5","key":"pcbi.1013436.ref039","doi-asserted-by":"crossref","first-page":"738","DOI":"10.1038\/nn.3689","article-title":"Serial dependence in visual perception","volume":"17","author":"J Fischer","year":"2014","journal-title":"Nat Neurosci"},{"issue":"2","key":"pcbi.1013436.ref040","doi-asserted-by":"crossref","first-page":"420","DOI":"10.1037\/0033-2909.86.2.420","article-title":"Intraclass correlations: uses in assessing rater reliability","volume":"86","author":"PE Shrout","year":"1979","journal-title":"Psychol Bull"},{"issue":"2","key":"pcbi.1013436.ref041","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1016\/j.jcm.2016.02.012","article-title":"A Guideline of Selecting and Reporting Intraclass Correlation Coefficients for Reliability Research","volume":"15","author":"TK Koo","year":"2016","journal-title":"J Chiropr Med"},{"issue":"1167","key":"pcbi.1013436.ref042","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1098\/rspb.1980.0020","article-title":"Theory of edge detection","volume":"207","author":"D Marr","year":"1980","journal-title":"Proc R Soc Lond B Biol Sci"},{"issue":"1","key":"pcbi.1013436.ref043","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1109\/TSMC.1979.4310076","article-title":"A Threshold Selection Method from Gray-Level Histograms","volume":"9","author":"N Otsu","year":"1979","journal-title":"IEEE Trans Syst, Man, Cybern"},{"issue":"4","key":"pcbi.1013436.ref044","doi-asserted-by":"crossref","first-page":"163","DOI":"10.1145\/37402.37422","article-title":"Marching cubes: A high resolution 3D surface construction algorithm","volume":"21","author":"WE Lorensen","year":"1987","journal-title":"SIGGRAPH Comput Graph"},{"key":"pcbi.1013436.ref045","doi-asserted-by":"crossref","DOI":"10.7717\/peerj.453","article-title":"scikit-image: image processing in Python","volume":"2","author":"S van der Walt","year":"2014","journal-title":"PeerJ"},{"key":"pcbi.1013436.ref046","doi-asserted-by":"crossref","DOI":"10.7554\/eLife.87065","article-title":"Independent regulation of Z-lines and M-lines during sarcomere assembly in cardiac myocytes revealed by the automatic image analysis software sarcApp","volume":"12","author":"AC Neininger-Castro","year":"2023","journal-title":"Elife"},{"key":"pcbi.1013436.ref047","doi-asserted-by":"crossref","first-page":"193","DOI":"10.1016\/j.actbio.2020.02.007","article-title":"Decoding rejuvenating effects of mechanical loading on skeletal aging using in vivo \u03bcCT imaging and deep learning","volume":"106","author":"P Asgharzadeh","year":"2020","journal-title":"Acta Biomater"},{"key":"pcbi.1013436.ref048","doi-asserted-by":"crossref","first-page":"25","DOI":"10.3389\/fcvm.2020.00025","article-title":"Deep Learning for Cardiac Image Segmentation: A Review","volume":"7","author":"C Chen","year":"2020","journal-title":"Front Cardiovasc Med"},{"key":"pcbi.1013436.ref049","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1016\/j.actbio.2024.12.062","article-title":"Machine learning identifies remodeling patterns in human lung extracellular matrix","volume":"195","author":"MJ Emerson","year":"2025","journal-title":"Acta Biomater"},{"key":"pcbi.1013436.ref050","doi-asserted-by":"crossref","first-page":"227","DOI":"10.1016\/j.actbio.2017.09.025","article-title":"A deep learning approach to estimate chemically-treated collagenous tissue nonlinear anisotropic stress-strain responses from microscopy images","volume":"63","author":"L Liang","year":"2017","journal-title":"Acta Biomater"},{"key":"pcbi.1013436.ref051","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1016\/j.media.2017.07.005","article-title":"A survey on deep learning in medical image analysis","volume":"42","author":"G Litjens","year":"2017","journal-title":"Med Image Anal"},{"key":"pcbi.1013436.ref052","doi-asserted-by":"crossref","unstructured":"Ronneberger O, Fischer P, Brox T. U-net: Convolutional networks for biomedical image segmentation. In: Medical image computing and computer-assisted intervention\u2013MICCAI 2015 : 18th international conference, Munich, Germany, October 5-9, 2015, proceedings, part III 18. Springer; 2015. p. 234\u2013241.","DOI":"10.1007\/978-3-319-24574-4_28"},{"issue":"6","key":"pcbi.1013436.ref053","doi-asserted-by":"crossref","first-page":"1719","DOI":"10.1109\/TSMCB.2012.2228639","article-title":"A generalized Laplacian of Gaussian filter for blob detection and its applications","volume":"43","author":"H Kong","year":"2013","journal-title":"IEEE Trans Cybern"},{"issue":"2","key":"pcbi.1013436.ref054","doi-asserted-by":"crossref","first-page":"373","DOI":"10.1007\/s10994-019-05855-6","article-title":"A survey on semi-supervised learning","volume":"109","author":"JE van Engelen","year":"2019","journal-title":"Mach Learn"},{"key":"pcbi.1013436.ref055","doi-asserted-by":"crossref","unstructured":"Zhai X, Oliver A, Kolesnikov A, Beyer L. S4L: Self-Supervised Semi-Supervised Learning. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV). 2019.","DOI":"10.1109\/ICCV.2019.00156"},{"key":"pcbi.1013436.ref056","doi-asserted-by":"crossref","unstructured":"Deng J, Dong W, Socher R, Li LJ, Li K, Fei-Fei L. Imagenet: A large-scale hierarchical image database. In: 2009 IEEE conference on computer vision and pattern recognition. Ieee; 2009. p. 248\u201355.","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"pcbi.1013436.ref057","unstructured":"Tan M, Le Q. EfficientNetV2: Smaller Models and Faster Training. In: Meila M, Zhang T, editors. Proceedings of the 38th International Conference on Machine Learning. vol. 139 of Proceedings of Machine Learning Research. PMLR; 2021. p. 10096\u2013106. Available from: https:\/\/proceedings.mlr.press\/v139\/tan21a.html"},{"key":"pcbi.1013436.ref058","unstructured":"Chen T, Kornblith S, Norouzi M, Hinton G. A Simple Framework for Contrastive Learning of Visual Representations. In: III HD, Singh A, editors. Proceedings of the 37th International Conference on Machine Learning. vol. 119 of Proceedings of Machine Learning Research. PMLR; 2020. p. 1597\u2013607. Available from: https:\/\/proceedings.mlr.press\/v119\/chen20j.html"},{"key":"pcbi.1013436.ref059","unstructured":"Oquab M, Darcet T, Moutakanni T, Vo H, Szafraniec M, Khalidov V, et al. Dinov2: Learning robust visual features without supervision. arXiv preprint arXiv:2304.07193. 2023."},{"key":"pcbi.1013436.ref060","doi-asserted-by":"crossref","first-page":"104749","DOI":"10.1016\/j.mechmat.2023.104749","article-title":"Investigating deep learning model calibration for classification problems in mechanics","volume":"184","author":"S Mohammadzadeh","year":"2023","journal-title":"Mechanics of Materials"},{"key":"pcbi.1013436.ref061","doi-asserted-by":"crossref","unstructured":"Chen YT, Shi J, Ye Z, Mertz C, Ramanan D, Kong S. Multimodal object detection via probabilistic ensembling. In: European Conference on Computer Vision. Springer; 2022. p. 139\u201358.","DOI":"10.1007\/978-3-031-20077-9_9"},{"issue":"2","key":"pcbi.1013436.ref062","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pcbi.1001088","article-title":"Self-organization of muscle cell structure and function","volume":"7","author":"A Grosberg","year":"2011","journal-title":"PLoS Comput Biol"},{"issue":"3","key":"pcbi.1013436.ref063","doi-asserted-by":"crossref","first-page":"282","DOI":"10.1016\/j.stemcr.2014.01.015","article-title":"Quality metrics for stem cell-derived cardiac myocytes","volume":"2","author":"SP Sheehy","year":"2014","journal-title":"Stem Cell Reports"},{"issue":"2","key":"pcbi.1013436.ref064","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1109\/TIT.1982.1056489","article-title":"Least squares quantization in PCM","volume":"28","author":"S Lloyd","year":"1982","journal-title":"IEEE Trans Inform Theory"},{"issue":"6","key":"pcbi.1013436.ref065","article-title":"Algorithms for hierarchical clustering: an overview, II","volume":"7","author":"F Murtagh","year":"2017","journal-title":"Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery"},{"key":"pcbi.1013436.ref066","unstructured":"Ester M, Kriegel HP, Sander J, Xu X, et al. A density-based algorithm for discovering clusters in large spatial databases with noise. In: kdd. vol. 96. 1996. p. 226\u2013231."},{"issue":"3","key":"pcbi.1013436.ref067","doi-asserted-by":"crossref","first-page":"660","DOI":"10.1109\/21.97458","article-title":"A survey of decision tree classifier methodology","volume":"21","author":"SR Safavian","year":"1991","journal-title":"IEEE Trans Syst Man Cybern"},{"key":"pcbi.1013436.ref068","unstructured":"Moshkovitz M, Dasgupta S, Rashtchian C, Frost N. Explainable k-means and k-medians clustering. In: International conference on machine learning. PMLR; 2020. p. 7055\u201365."},{"key":"pcbi.1013436.ref069","unstructured":"Frost N, Moshkovitz M, Rashtchian C. ExKMC: Expanding Explainable k-Means Clustering. arXiv preprint arXiv:2006.02399. 2020."}],"updated-by":[{"DOI":"10.1371\/journal.pcbi.1013436","type":"new_version","label":"New version","source":"publisher","updated":{"date-parts":[[2025,10,14]],"date-time":"2025-10-14T00:00:00Z","timestamp":1760400000000}}],"container-title":["PLOS Computational Biology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pcbi.1013436","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,14]],"date-time":"2025-10-14T17:52:31Z","timestamp":1760464351000},"score":1,"resource":{"primary":{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pcbi.1013436"}},"subtitle":[],"editor":[{"given":"Anna","family":"Grosberg","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"editor"}]}],"short-title":[],"issued":{"date-parts":[[2025,10,3]]},"references-count":69,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2025,10,3]]}},"URL":"https:\/\/doi.org\/10.1371\/journal.pcbi.1013436","relation":{"new_version":[{"id-type":"doi","id":"10.1371\/journal.pcbi.1013436","asserted-by":"object"}]},"ISSN":["1553-7358"],"issn-type":[{"value":"1553-7358","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10,3]]}}}