{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,22]],"date-time":"2026-02-22T03:58:19Z","timestamp":1771732699773,"version":"3.50.1"},"update-to":[{"DOI":"10.1371\/journal.pcbi.1012283","type":"new_version","label":"New version","source":"publisher","updated":{"date-parts":[[2024,7,30]],"date-time":"2024-07-30T00:00:00Z","timestamp":1722297600000}}],"reference-count":67,"publisher":"Public Library of Science (PLoS)","issue":"7","license":[{"start":{"date-parts":[[2024,7,18]],"date-time":"2024-07-18T00:00:00Z","timestamp":1721260800000},"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":["NSFNCS-FR 1926781"],"award-info":[{"award-number":["NSFNCS-FR 1926781"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Baszucki Brain Research Fund"},{"name":"National Institute of Health","award":["T32-GM008444"],"award-info":[{"award-number":["T32-GM008444"]}]},{"name":"Marie-Jos\u00e9e Kravis Fellowship"}],"content-domain":{"domain":["www.ploscompbiol.org"],"crossmark-restriction":false},"short-container-title":["PLoS Comput Biol"],"abstract":"<jats:p>All fields of science depend on mathematical models. <jats:italic>Occam\u2019s razor<\/jats:italic> refers to the principle that good models should exclude parameters beyond those minimally required to describe the systems they represent. This is because redundancy can lead to incorrect estimates of model parameters from data, and thus inaccurate or ambiguous conclusions. Here, we show how deep learning can be powerfully leveraged to apply Occam\u2019s razor to model parameters. Our method, FixFit, uses a feedforward deep neural network with a bottleneck layer to characterize and predict the behavior of a given model from its input parameters. FixFit has three major benefits. First, it provides a metric to quantify the original model\u2019s degree of complexity. Second, it allows for the unique fitting of data. Third, it provides an unbiased way to discriminate between experimental hypotheses that add value versus those that do not. In three use cases, we demonstrate the broad applicability of this method across scientific domains. To validate the method using a known system, we apply FixFit to recover known composite parameters for the Kepler orbit model and a dynamic model of blood glucose regulation. In the latter, we demonstrate the ability to fit the latent parameters to real data. To illustrate how the method can be applied to less well-established fields, we use it to identify parameters for a multi-scale brain model and reduce the search space for viable candidate mechanisms.<\/jats:p>","DOI":"10.1371\/journal.pcbi.1012283","type":"journal-article","created":{"date-parts":[[2024,7,18]],"date-time":"2024-07-18T17:56:58Z","timestamp":1721325418000},"page":"e1012283","update-policy":"https:\/\/doi.org\/10.1371\/journal.pcbi.corrections_policy","source":"Crossref","is-referenced-by-count":6,"title":["Achieving Occam\u2019s razor: Deep learning for optimal model reduction"],"prefix":"10.1371","volume":"20","author":[{"given":"Botond B.","family":"Antal","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Anthony G.","family":"Chesebro","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Helmut H.","family":"Strey","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lilianne R.","family":"Mujica-Parodi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6044-2511","authenticated-orcid":true,"given":"Corey","family":"Weistuch","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"340","published-online":{"date-parts":[[2024,7,18]]},"reference":[{"issue":"5","key":"pcbi.1012283.ref001","doi-asserted-by":"crossref","first-page":"2003","DOI":"10.1140\/epjc\/s10052-012-2003-4","article-title":"Updated status of the global electroweak fit and constraints on new physics","volume":"72","author":"G Group","year":"2012","journal-title":"The European Physical Journal C"},{"key":"pcbi.1012283.ref002","doi-asserted-by":"crossref","unstructured":"D\u2019haeseleer P, Wen X, Fuhrman S, Somogyi R. Linear modeling of mRNA expression levels during CNS development and injury. In: Biocomputing\u201999. World Scientific; 1999. p. 41\u201352.","DOI":"10.1142\/9789814447300_0005"},{"issue":"4","key":"pcbi.1012283.ref003","doi-asserted-by":"crossref","first-page":"449","DOI":"10.1113\/jphysiol.1952.sp004717","article-title":"Currents carried by sodium and potassium ions through the membrane of the giant axon of Loligo","volume":"116","author":"AL Hodgkin","year":"1952","journal-title":"The Journal of physiology"},{"issue":"12","key":"pcbi.1012283.ref004","doi-asserted-by":"crossref","first-page":"1345","DOI":"10.1038\/nn1352","article-title":"Similar network activity from disparate circuit parameters","volume":"7","author":"AA Prinz","year":"2004","journal-title":"Nature neuroscience"},{"issue":"3","key":"pcbi.1012283.ref005","doi-asserted-by":"crossref","first-page":"031029","DOI":"10.1103\/PhysRevX.12.031029","article-title":"DeepLSS: Breaking Parameter Degeneracies in Large-Scale Structure with Deep-Learning Analysis of Combined Probes","volume":"12","author":"T Kacprzak","year":"2022","journal-title":"Physical Review X"},{"issue":"10","key":"pcbi.1012283.ref006","doi-asserted-by":"crossref","first-page":"e189","DOI":"10.1371\/journal.pcbi.0030189","article-title":"Universally sloppy parameter sensitivities in systems biology models","volume":"3","author":"RN Gutenkunst","year":"2007","journal-title":"PLoS computational biology"},{"issue":"11","key":"pcbi.1012283.ref007","doi-asserted-by":"crossref","first-page":"e27755","DOI":"10.1371\/journal.pone.0027755","article-title":"Structural identifiability of systems biology models: a critical comparison of methods","volume":"6","author":"OT Chis","year":"2011","journal-title":"PloS one"},{"issue":"1","key":"pcbi.1012283.ref008","doi-asserted-by":"crossref","DOI":"10.1063\/1.4923066","article-title":"Perspective: Sloppiness and emergent theories in physics, biology, and beyond","volume":"143","author":"MK Transtrum","year":"2015","journal-title":"The Journal of chemical physics"},{"key":"pcbi.1012283.ref009","unstructured":"Weise T. Global optimization algorithms-theory and application. Self-Published Thomas Weise. 2009;361."},{"key":"pcbi.1012283.ref010","first-page":"013005","article-title":"Speed Inversion in a Potts Glass Model of Cortical Dynamics","volume":"1","author":"KI Ryom","year":"2023","journal-title":"P R X Life"},{"key":"pcbi.1012283.ref011","doi-asserted-by":"crossref","unstructured":"Mor\u00e9 JJ. The Levenberg-Marquardt algorithm: implementation and theory. In: Numerical Analysis: Proceedings of the Biennial Conference Held at Dundee, June 28\u2013July 1, 1977. Springer; 2006. p. 105\u2013116.","DOI":"10.1007\/BFb0067700"},{"key":"pcbi.1012283.ref012","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1016\/j.coisb.2021.03.005","article-title":"On structural and practical identifiability","volume":"25","author":"FG Wieland","year":"2021","journal-title":"Current Opinion in Systems Biology"},{"issue":"1-2","key":"pcbi.1012283.ref013","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1016\/0025-5564(78)90063-9","article-title":"System identifiability based on the power series expansion of the solution","volume":"41","author":"H Pohjanpalo","year":"1978","journal-title":"Mathematical biosciences"},{"key":"pcbi.1012283.ref014","doi-asserted-by":"crossref","DOI":"10.1201\/9781315120003","volume-title":"Parameter redundancy and identifiability","author":"D Cole","year":"2020"},{"issue":"2","key":"pcbi.1012283.ref015","doi-asserted-by":"crossref","first-page":"233","DOI":"10.1002\/aic.690370209","article-title":"Nonlinear principal component analysis using autoassociative neural networks","volume":"37","author":"MA Kramer","year":"1991","journal-title":"AIChE journal"},{"key":"pcbi.1012283.ref016","unstructured":"Tishby N, Pereira FC, Bialek W. The information bottleneck method. arXiv preprint physics\/0004057. 2000;."},{"key":"pcbi.1012283.ref017","doi-asserted-by":"crossref","unstructured":"Tishby N, Zaslavsky N. Deep learning and the information bottleneck principle. In: 2015 ieee information theory workshop (itw). IEEE; 2015. p. 1\u20135.","DOI":"10.1109\/ITW.2015.7133169"},{"key":"pcbi.1012283.ref018","unstructured":"Achille A, Soatto S. On the emergence of invariance and disentangling in deep representations. arXiv preprint arXiv:170601350. 2017;125:126\u2013127."},{"issue":"5","key":"pcbi.1012283.ref019","doi-asserted-by":"crossref","first-page":"359","DOI":"10.1016\/0893-6080(89)90020-8","article-title":"Multilayer feedforward networks are universal approximators","volume":"2","author":"K Hornik","year":"1989","journal-title":"Neural networks"},{"issue":"2","key":"pcbi.1012283.ref020","doi-asserted-by":"crossref","first-page":"251","DOI":"10.1016\/0893-6080(91)90009-T","article-title":"Approximation capabilities of multilayer feedforward networks","volume":"4","author":"K Hornik","year":"1991","journal-title":"Neural networks"},{"issue":"48","key":"pcbi.1012283.ref021","first-page":"7","article-title":"Approximation with artificial neural networks","volume":"24","author":"BC Cs\u00e1ji","year":"2001","journal-title":"Faculty of Sciences, Etvs Lornd University, Hungary"},{"issue":"19","key":"pcbi.1012283.ref022","doi-asserted-by":"crossref","first-page":"6022","DOI":"10.1021\/jp9096919","article-title":"Global sensitivity analysis for systems with independent and\/or correlated inputs","volume":"114","author":"G Li","year":"2010","journal-title":"The journal of physical chemistry A"},{"issue":"3","key":"pcbi.1012283.ref023","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1038\/s41592-019-0686-2","article-title":"SciPy 1.0: fundamental algorithms for scientific computing in Python","volume":"17","author":"P Virtanen","year":"2020","journal-title":"Nature methods"},{"key":"pcbi.1012283.ref024","volume-title":"Fundamentals of astrodynamics","author":"RR Bate","year":"2020"},{"issue":"3","key":"pcbi.1012283.ref025","doi-asserted-by":"crossref","first-page":"795","DOI":"10.1063\/1.166453","article-title":"A coupled ordinary differential equation lattice model for the simulation of epileptic seizures","volume":"9","author":"R Larter","year":"1999","journal-title":"Chaos: An Interdisciplinary Journal of Nonlinear Science"},{"issue":"4","key":"pcbi.1012283.ref026","doi-asserted-by":"crossref","first-page":"703","DOI":"10.1088\/0954-898X_14_4_305","article-title":"Modulation of excitatory synaptic coupling facilitates synchronization and complex dynamics in a biophysical model of neuronal dynamics","volume":"14","author":"M Breakspear","year":"2003","journal-title":"Network: Computation in Neural Systems"},{"issue":"11","key":"pcbi.1012283.ref027","doi-asserted-by":"crossref","first-page":"886","DOI":"10.15252\/msb.20167216","article-title":"Dynamical compensation in physiological circuits","volume":"12","author":"O Karin","year":"2016","journal-title":"Mol Syst Biol"},{"issue":"4","key":"pcbi.1012283.ref028","doi-asserted-by":"crossref","first-page":"605","DOI":"10.1006\/jtbi.2000.2150","article-title":"A model of beta-cell mass, insulin, and glucose kinetics: pathways to diabetes","volume":"206","author":"B Topp","year":"2000","journal-title":"J Theor Biol"},{"issue":"2","key":"pcbi.1012283.ref029","doi-asserted-by":"crossref","first-page":"442","DOI":"10.1172\/JCI113339","article-title":"Twenty-four-hour profiles and pulsatile patterns of insulin secretion in normal and obese subjects","volume":"81","author":"KS Polonsky","year":"1988","journal-title":"J Clin Invest"},{"issue":"7197","key":"pcbi.1012283.ref030","doi-asserted-by":"crossref","first-page":"869","DOI":"10.1038\/nature06976","article-title":"What we can do and what we cannot do with fMRI","volume":"453","author":"NK Logothetis","year":"2008","journal-title":"Nature"},{"key":"pcbi.1012283.ref031","doi-asserted-by":"crossref","first-page":"113120","DOI":"10.1016\/j.chaos.2023.113120","article-title":"Ion gradient-driven bifurcations of a multi-scale neuronal model","volume":"167","author":"AG Chesebro","year":"2023","journal-title":"Chaos, Solitons & Fractals"},{"key":"pcbi.1012283.ref032","doi-asserted-by":"crossref","first-page":"91","DOI":"10.3389\/fncom.2019.00091","article-title":"Evaluation of resting spatio-temporal dynamics of a neural mass model using resting fMRI connectivity and EEG microstates","volume":"13","author":"H Endo","year":"2020","journal-title":"Frontiers in computational neuroscience"},{"issue":"4","key":"pcbi.1012283.ref033","doi-asserted-by":"crossref","first-page":"537","DOI":"10.1002\/mrm.1910340409","article-title":"Functional connectivity in the motor cortex of resting human brain using echo-planar MRI","volume":"34","author":"B Biswal","year":"1995","journal-title":"Magnetic resonance in medicine"},{"issue":"2","key":"pcbi.1012283.ref034","doi-asserted-by":"crossref","first-page":"85","DOI":"10.59566\/IJBS.2014.10085","article-title":"Brain Na+, K+-ATPase activity in aging and disease","volume":"10","author":"GR de Lores Arnaiz","year":"2014","journal-title":"International journal of biomedical science: IJBS"},{"issue":"4","key":"pcbi.1012283.ref035","doi-asserted-by":"crossref","first-page":"1122","DOI":"10.1190\/1.1444411","article-title":"What is noise?","volume":"63","author":"JA Scales","year":"1998","journal-title":"Geophysics"},{"issue":"15","key":"pcbi.1012283.ref036","doi-asserted-by":"crossref","first-page":"1923","DOI":"10.1093\/bioinformatics\/btp358","article-title":"Structural and practical identifiability analysis of partially observed dynamical models by exploiting the profile likelihood","volume":"25","author":"A Raue","year":"2009","journal-title":"Bioinformatics"},{"issue":"11","key":"pcbi.1012283.ref037","doi-asserted-by":"crossref","first-page":"1079","DOI":"10.1119\/1.18725","article-title":"Paul langevin\u2019s 1908 paper \u201con the theory of brownian motion\u201d [\u201csur la th\u00e9orie du mouvement brownien,\u201d cr acad. sci.(paris) 146, 530\u2013533 (1908)]","volume":"65","author":"DS Lemons","year":"1997","journal-title":"American Journal of Physics"},{"key":"pcbi.1012283.ref038","volume-title":"Introduction to statistical time series","author":"WA Fuller","year":"2009"},{"key":"pcbi.1012283.ref039","unstructured":"Tang C, Salakhutdinov RR. Learning stochastic feedforward neural networks. Advances in Neural Information Processing Systems. 2013;26."},{"issue":"2","key":"pcbi.1012283.ref040","doi-asserted-by":"crossref","first-page":"240","DOI":"10.1109\/72.279188","article-title":"Recurrent neural networks and robust time series prediction","volume":"5","author":"JT Connor","year":"1994","journal-title":"IEEE transactions on neural networks"},{"issue":"3","key":"pcbi.1012283.ref041","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1007\/BF02289233","article-title":"The varimax criterion for analytic rotation in factor analysis","volume":"23","author":"HF Kaiser","year":"1958","journal-title":"Psychometrika"},{"key":"pcbi.1012283.ref042","doi-asserted-by":"crossref","unstructured":"Xu W, Liu X, Gong Y. Document clustering based on non-negative matrix factorization. In: Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval; 2003. p. 267\u2013273.","DOI":"10.1145\/860435.860485"},{"key":"pcbi.1012283.ref043","doi-asserted-by":"crossref","unstructured":"Ghasedi Dizaji K, Herandi A, Deng C, Cai W, Huang H. Deep clustering via joint convolutional autoencoder embedding and relative entropy minimization. In: Proceedings of the IEEE international conference on computer vision; 2017. p. 5736\u20135745.","DOI":"10.1109\/ICCV.2017.612"},{"issue":"7","key":"pcbi.1012283.ref044","doi-asserted-by":"crossref","first-page":"433","DOI":"10.1038\/s43588-022-00281-6","article-title":"Automated discovery of fundamental variables hidden in experimental data","volume":"2","author":"B Chen","year":"2022","journal-title":"Nature Computational Science"},{"key":"pcbi.1012283.ref045","doi-asserted-by":"crossref","first-page":"105859","DOI":"10.1016\/j.asoc.2019.105859","article-title":"A novel feature selection method based on global sensitivity analysis with application in machine learning-based prediction model","volume":"85","author":"P Zhang","year":"2019","journal-title":"Applied Soft Computing"},{"issue":"44","key":"pcbi.1012283.ref046","doi-asserted-by":"crossref","first-page":"22071","DOI":"10.1073\/pnas.1900654116","article-title":"Definitions, methods, and applications in interpretable machine learning","volume":"116","author":"WJ Murdoch","year":"2019","journal-title":"Proceedings of the National Academy of Sciences"},{"key":"pcbi.1012283.ref047","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.dsp.2017.10.011","article-title":"Methods for interpreting and understanding deep neural networks","volume":"73","author":"G Montavon","year":"2018","journal-title":"Digital signal processing"},{"key":"pcbi.1012283.ref048","doi-asserted-by":"crossref","unstructured":"Selvaraju RR, Cogswell M, Das A, Vedantam R, Parikh D, Batra D. Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE international conference on computer vision; 2017. p. 618\u2013626.","DOI":"10.1109\/ICCV.2017.74"},{"issue":"5923","key":"pcbi.1012283.ref049","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1126\/science.1165893","article-title":"Distilling free-form natural laws from experimental data","volume":"324","author":"M Schmidt","year":"2009","journal-title":"science"},{"key":"pcbi.1012283.ref050","unstructured":"La Cava W, Orzechowski P, Burlacu B, de Fran\u00e7a FO, Virgolin M, Jin Y, et al. Contemporary symbolic regression methods and their relative performance. arXiv preprint arXiv:210714351. 2021;."},{"key":"pcbi.1012283.ref051","unstructured":"Petersen BK, Larma ML, Mundhenk TN, Santiago CP, Kim SK, Kim JT. Deep symbolic regression: Recovering mathematical expressions from data via risk-seeking policy gradients. arXiv preprint arXiv:191204871. 2019;."},{"issue":"9","key":"pcbi.1012283.ref052","doi-asserted-by":"crossref","first-page":"4166","DOI":"10.1109\/TNNLS.2020.3017010","article-title":"Integration of neural network-based symbolic regression in deep learning for scientific discovery","volume":"32","author":"S Kim","year":"2020","journal-title":"IEEE transactions on neural networks and learning systems"},{"key":"pcbi.1012283.ref053","first-page":"273","article-title":"Bayesian experimental design: A review","author":"K Chaloner","year":"1995","journal-title":"Statistical science"},{"issue":"1","key":"pcbi.1012283.ref054","doi-asserted-by":"crossref","first-page":"e1002888","DOI":"10.1371\/journal.pcbi.1002888","article-title":"Maximizing the information content of experiments in systems biology","volume":"9","author":"J Liepe","year":"2013","journal-title":"PLoS computational biology"},{"issue":"8","key":"pcbi.1012283.ref055","doi-asserted-by":"crossref","first-page":"559","DOI":"10.1038\/s41592-018-0083-2","article-title":"Optimal experimental design","volume":"15","author":"B Smucker","year":"2018","journal-title":"Nat Methods"},{"issue":"1","key":"pcbi.1012283.ref056","doi-asserted-by":"crossref","first-page":"194","DOI":"10.1137\/22M1469067","article-title":"Differential Elimination for Dynamical Models via Projections with Applications to Structural Identifiability","volume":"7","author":"R Dong","year":"2023","journal-title":"SIAM Journal on Applied Algebra and Geometry"},{"issue":"3","key":"pcbi.1012283.ref057","doi-asserted-by":"crossref","first-page":"968","DOI":"10.1016\/j.neuroimage.2006.01.021","article-title":"An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest","volume":"31","author":"RS Desikan","year":"2006","journal-title":"Neuroimage"},{"issue":"4","key":"pcbi.1012283.ref058","doi-asserted-by":"crossref","first-page":"1273","DOI":"10.1016\/S1053-8119(03)00202-7","article-title":"Dynamic causal modelling","volume":"19","author":"KJ Friston","year":"2003","journal-title":"Neuroimage"},{"issue":"Suppl 1","key":"pcbi.1012283.ref059","doi-asserted-by":"crossref","first-page":"S97","DOI":"10.2337\/dc23-S006","article-title":"6. Glycemic targets: Standards of care in diabetes-2023","volume":"46","author":"NA ElSayed","year":"2023","journal-title":"Diabetes Care"},{"issue":"11","key":"pcbi.1012283.ref060","doi-asserted-by":"crossref","first-page":"6170","DOI":"10.1073\/pnas.1913042117","article-title":"Diet modulates brain network stability, a biomarker for brain aging, in young adults","volume":"117","author":"LR Mujica-Parodi","year":"2020","journal-title":"Proceedings of the National Academy of Sciences"},{"issue":"1","key":"pcbi.1012283.ref061","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1002\/mrm.1910350114","article-title":"Reduction of physiological fluctuations in fMRI using digital filters","volume":"35","author":"B Biswal","year":"1996","journal-title":"Magnetic resonance in medicine"},{"key":"pcbi.1012283.ref062","unstructured":"Lu Z, Pu H, Wang F, Hu Z, Wang L. The expressive power of neural networks: A view from the width. Advances in neural information processing systems. 2017;30."},{"key":"pcbi.1012283.ref063","unstructured":"Kidger P, Lyons T. Universal approximation with deep narrow networks. In: Conference on learning theory. PMLR; 2020. p. 2306\u20132327."},{"key":"pcbi.1012283.ref064","unstructured":"Abadi M, Agarwal A, Barham P, Brevdo E, Chen Z, Citro C, et al. TensorFlow: Large-scale machine learning on heterogeneous systems; 2015."},{"key":"pcbi.1012283.ref065","unstructured":"Kingma DP, Ba J. Adam: A method for stochastic optimization. arXiv preprint arXiv:14126980. 2014;."},{"issue":"9","key":"pcbi.1012283.ref066","doi-asserted-by":"crossref","DOI":"10.21105\/joss.00097","article-title":"SALib: An open-source Python library for Sensitivity Analysis","volume":"2","author":"J Herman","year":"2017","journal-title":"The Journal of Open Source Software"},{"key":"pcbi.1012283.ref067","doi-asserted-by":"crossref","first-page":"18155","DOI":"10.18174\/sesmo.18155","article-title":"Toward SALib 2.0: Advancing the accessibility and interpretability of global sensitivity analyses","volume":"4","author":"T Iwanaga","year":"2022","journal-title":"Socio-Environmental Systems Modelling"}],"updated-by":[{"DOI":"10.1371\/journal.pcbi.1012283","type":"new_version","label":"New version","source":"publisher","updated":{"date-parts":[[2024,7,30]],"date-time":"2024-07-30T00:00:00Z","timestamp":1722297600000}}],"container-title":["PLOS Computational Biology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pcbi.1012283","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,30]],"date-time":"2024-07-30T17:43:49Z","timestamp":1722361429000},"score":1,"resource":{"primary":{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pcbi.1012283"}},"subtitle":[],"editor":[{"given":"Marieke Karlijn","family":"van Vugt","sequence":"first","affiliation":[],"role":[{"role":"editor","vocabulary":"crossref"}]}],"short-title":[],"issued":{"date-parts":[[2024,7,18]]},"references-count":67,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2024,7,18]]}},"URL":"https:\/\/doi.org\/10.1371\/journal.pcbi.1012283","relation":{"new_version":[{"id-type":"doi","id":"10.1371\/journal.pcbi.1012283","asserted-by":"object"}]},"ISSN":["1553-7358"],"issn-type":[{"value":"1553-7358","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,7,18]]}}}