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Parallel advances in genetics and histomechanical characterization provide significant insight into these conditions, but there remains a pressing need to integrate such information. We present a novel genotype-to-biomechanical phenotype neural network (G2\u03a6net) for characterizing and classifying biomechanical properties of soft tissues, which serve as important functional readouts of tissue health or disease. We illustrate the utility of our approach by inferring the nonlinear, genotype-dependent constitutive behavior of the aorta for four mouse models involving defects or deficiencies in extracellular constituents. We show that G2\u03a6net can infer the biomechanical response while simultaneously ascribing the associated genotype by utilizing limited, noisy, and unstructured experimental data. 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Simple and scalable predictive uncertainty estimation using deep ensembles. arXiv preprint arXiv:161201474. 2016."},{"key":"pcbi.1010660.ref045","unstructured":"Zhang H, Cisse M, Dauphin YN, Lopez-Paz D. mixup: Beyond empirical risk minimization. arXiv preprint arXiv:171009412. 2017."},{"issue":"8","key":"pcbi.1010660.ref046","doi-asserted-by":"crossref","first-page":"681","DOI":"10.1139\/o57-080","article-title":"The reason for the shape of the distensibility curves of arteries","volume":"35","author":"MR Roach","year":"1957","journal-title":"Canadian Journal of Biochemistry and Physiology"},{"issue":"5","key":"pcbi.1010660.ref047","doi-asserted-by":"crossref","first-page":"1852","DOI":"10.1073\/pnas.94.5.1852","article-title":"Type III collagen is crucial for collagen I fibrillogenesis and for normal cardiovascular development","volume":"94","author":"X Liu","year":"1997","journal-title":"Proceedings of the National Academy of Sciences"},{"issue":"2","key":"pcbi.1010660.ref048","doi-asserted-by":"crossref","first-page":"565","DOI":"10.1167\/iovs.05-0771","article-title":"Structural abnormalities of the cornea and lid resulting from collagen V mutations","volume":"47","author":"F Segev","year":"2006","journal-title":"Investigative Pphthalmology & Visual Science"},{"key":"pcbi.1010660.ref049","doi-asserted-by":"crossref","first-page":"397","DOI":"10.1016\/j.jmbbm.2017.01.045","article-title":"A hidden structural vulnerability in the thrombospondin-2 deficient aorta increases the propensity to intramural delamination","volume":"71","author":"C Bellini","year":"2017","journal-title":"Journal of the Mechanical Behavior of Biomedical Materials"},{"issue":"10","key":"pcbi.1010660.ref050","doi-asserted-by":"crossref","first-page":"1783","DOI":"10.1172\/JCI4487","article-title":"Novel arterial pathology in mice and humans hemizygous for elastin","volume":"102","author":"DY Li","year":"1998","journal-title":"The Journal of Clinical Investigation"},{"issue":"5","key":"pcbi.1010660.ref051","doi-asserted-by":"crossref","first-page":"523","DOI":"10.1161\/CIRCRESAHA.107.153510","article-title":"Functional rescue of elastin insufficiency in mice by the human elastin gene: implications for mouse models of human disease","volume":"101","author":"E Hirano","year":"2007","journal-title":"Circulation Research"},{"issue":"5","key":"pcbi.1010660.ref052","doi-asserted-by":"crossref","first-page":"512","DOI":"10.1161\/CIRCRESAHA.107.157776","article-title":"Loss of elastic fiber integrity and reduction of vascular smooth muscle contraction resulting from the upregulated activities of matrix metalloproteinase-2 and-9 in the thoracic aortic aneurysm in Marfan syndrome","volume":"101","author":"AW Chung","year":"2007","journal-title":"Circulation Research"},{"issue":"3","key":"pcbi.1010660.ref053","first-page":"1329","article-title":"Abnormal muscle mechanosignaling triggers cardiomyopathy in mice with Marfan syndrome","volume":"124","author":"JR Cook","year":"2014","journal-title":"The Journal of Clinical Investigation"},{"issue":"3","key":"pcbi.1010660.ref054","doi-asserted-by":"crossref","first-page":"583","DOI":"10.1161\/CIRCRESAHA.109.207852","article-title":"Fibulin-4 deficiency results in ascending aortic aneurysms: a potential link between abnormal smooth muscle cell phenotype and aneurysm progression","volume":"106","author":"J Huang","year":"2010","journal-title":"Circulation Research"},{"issue":"3","key":"pcbi.1010660.ref055","doi-asserted-by":"crossref","first-page":"031007","DOI":"10.1115\/1.4029431","article-title":"Decreased elastic energy storage, not increased material stiffness, characterizes central artery dysfunction in fibulin-5 deficiency independent of sex","volume":"137","author":"J Ferruzzi","year":"2015","journal-title":"Journal of Biomechanical Engineering"},{"issue":"1","key":"pcbi.1010660.ref056","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.jacc.2006.12.050","article-title":"Mechanical factors in arterial aging: a clinical perspective","volume":"50","author":"MF O\u2019rourke","year":"2007","journal-title":"Journal of the American College of Cardiology"},{"issue":"2","key":"pcbi.1010660.ref057","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1002\/path.2101","article-title":"Ageing of the conduit arteries","volume":"211","author":"S Greenwald","year":"2007","journal-title":"The Journal of Pathology: A Journal of the Pathological Society of Great Britain and Ireland"},{"key":"pcbi.1010660.ref058","doi-asserted-by":"crossref","first-page":"369","DOI":"10.1016\/j.jmbbm.2017.11.035","article-title":"Predictive capabilities of various constitutive models for arterial tissue","volume":"78","author":"F Schroeder","year":"2018","journal-title":"Journal of the Mechanical Behavior of Biomedical 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Unsupervised physics-informed disentanglement of multimodal data for high-throughput scientific discovery. arXiv preprint arXiv:220203242. 2022."},{"key":"pcbi.1010660.ref062","doi-asserted-by":"crossref","unstructured":"Meng X, Yang L, Mao Z, Ferrandis JdA, Karniadakis GE. Learning Functional Priors and Posteriors from Data and Physics. arXiv preprint arXiv:210605863. 2021.","DOI":"10.1016\/j.jcp.2022.111073"},{"issue":"7","key":"pcbi.1010660.ref063","doi-asserted-by":"crossref","first-page":"3819","DOI":"10.1073\/pnas.96.7.3819","article-title":"Pathogenetic sequence for aneurysm revealed in mice underexpressing fibrillin-1","volume":"96","author":"L Pereira","year":"1999","journal-title":"Proceedings of the National Academy of Sciences"},{"issue":"6868","key":"pcbi.1010660.ref064","doi-asserted-by":"crossref","first-page":"168","DOI":"10.1038\/415168a","article-title":"Fibulin-5 is an elastin-binding protein essential for elastic fibre development in vivo","volume":"415","author":"H Yanagisawa","year":"2002","journal-title":"Nature"},{"issue":"180","key":"pcbi.1010660.ref065","doi-asserted-by":"crossref","first-page":"20210336","DOI":"10.1098\/rsif.2021.0336","article-title":"Excessive adventitial stress drives inflammation-mediated fibrosis in hypertensive aortic remodelling in mice","volume":"18","author":"B Spronck","year":"2021","journal-title":"Journal of the Royal Society Interface"},{"issue":"1","key":"pcbi.1010660.ref066","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1007\/s10237-018-1080-1","article-title":"Absence of LTBP-3 attenuates the aneurysmal phenotype but not spinal effects on the aorta in Marfan syndrome","volume":"18","author":"A Korneva","year":"2019","journal-title":"Biomechanics and Modeling in Mechanobiology"},{"issue":"7","key":"pcbi.1010660.ref067","doi-asserted-by":"crossref","first-page":"1312","DOI":"10.1097\/HJH.0000000000002400","article-title":"Aortic remodeling is modest and sex-independent in mice when hypertension is superimposed on aging","volume":"38","author":"B Spronck","year":"2020","journal-title":"Journal of Hypertension"},{"key":"pcbi.1010660.ref068","doi-asserted-by":"crossref","DOI":"10.1007\/978-0-387-21576-1","volume-title":"Cardiovascular Solid Mechanics: Cells, Tissues, and Organs","author":"JD Humphrey","year":"2002"}],"updated-by":[{"DOI":"10.1371\/journal.pcbi.1010660","type":"new_version","label":"New version","source":"publisher","updated":{"date-parts":[[2022,11,16]],"date-time":"2022-11-16T00:00:00Z","timestamp":1668556800000}}],"container-title":["PLOS Computational Biology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pcbi.1010660","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,3,10]],"date-time":"2023-03-10T20:10:08Z","timestamp":1678479008000},"score":1,"resource":{"primary":{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pcbi.1010660"}},"subtitle":[],"editor":[{"given":"Alison L.","family":"Marsden","sequence":"first","affiliation":[]}],"short-title":[],"issued":{"date-parts":[[2022,10,31]]},"references-count":68,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2022,10,31]]}},"URL":"https:\/\/doi.org\/10.1371\/journal.pcbi.1010660","relation":{"new_version":[{"id-type":"doi","id":"10.1371\/journal.pcbi.1010660","asserted-by":"object"}]},"ISSN":["1553-7358"],"issn-type":[{"value":"1553-7358","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,10,31]]}}}