{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,3]],"date-time":"2026-05-03T10:15:18Z","timestamp":1777803318514,"version":"3.51.4"},"reference-count":56,"publisher":"Springer Science and Business Media LLC","issue":"12","license":[{"start":{"date-parts":[[2022,12,19]],"date-time":"2022-12-19T00:00:00Z","timestamp":1671408000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,12,19]],"date-time":"2022-12-19T00:00:00Z","timestamp":1671408000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/100000185","name":"United States Department of Defense | Defense Advanced Research Projects Agency","doi-asserted-by":"publisher","award":["HR00112290029"],"award-info":[{"award-number":["HR00112290029"]}],"id":[{"id":"10.13039\/100000185","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000185","name":"United States Department of Defense | Defense Advanced Research Projects Agency","doi-asserted-by":"publisher","award":["HR00112290029"],"award-info":[{"award-number":["HR00112290029"]}],"id":[{"id":"10.13039\/100000185","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000185","name":"United States Department of Defense | Defense Advanced Research Projects Agency","doi-asserted-by":"publisher","award":["HR00112290029"],"award-info":[{"award-number":["HR00112290029"]}],"id":[{"id":"10.13039\/100000185","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000006","name":"United States Department of Defense | United States Navy | Office of Naval Research","doi-asserted-by":"publisher","award":["N00014-20-1-2366"],"award-info":[{"award-number":["N00014-20-1-2366"]}],"id":[{"id":"10.13039\/100000006","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000006","name":"United States Department of Defense | United States Navy | Office of Naval Research","doi-asserted-by":"publisher","award":["N00014-21-1-2357"],"award-info":[{"award-number":["N00014-21-1-2357"]}],"id":[{"id":"10.13039\/100000006","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000006","name":"United States Department of Defense | United States Navy | Office of Naval Research","doi-asserted-by":"publisher","award":["N00014-20-1-2366"],"award-info":[{"award-number":["N00014-20-1-2366"]}],"id":[{"id":"10.13039\/100000006","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000006","name":"United States Department of Defense | United States Navy | Office of Naval Research","doi-asserted-by":"publisher","award":["N00014-21-1-2357"],"award-info":[{"award-number":["N00014-21-1-2357"]}],"id":[{"id":"10.13039\/100000006","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000006","name":"United States Department of Defense | United States Navy | Office of Naval Research","doi-asserted-by":"publisher","award":["N00014-20-1-2366"],"award-info":[{"award-number":["N00014-20-1-2366"]}],"id":[{"id":"10.13039\/100000006","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000006","name":"United States Department of Defense | United States Navy | Office of Naval Research","doi-asserted-by":"publisher","award":["N00014-21-1-2357"],"award-info":[{"award-number":["N00014-21-1-2357"]}],"id":[{"id":"10.13039\/100000006","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Nat Comput Sci"],"DOI":"10.1038\/s43588-022-00376-0","type":"journal-article","created":{"date-parts":[[2022,12,19]],"date-time":"2022-12-19T12:31:45Z","timestamp":1671453105000},"page":"823-833","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":64,"title":["Discovering and forecasting extreme events via active learning in neural operators"],"prefix":"10.1038","volume":"2","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4485-6359","authenticated-orcid":false,"given":"Ethan","family":"Pickering","sequence":"first","affiliation":[]},{"given":"Stephen","family":"Guth","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9713-7120","authenticated-orcid":false,"given":"George Em","family":"Karniadakis","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0302-0691","authenticated-orcid":false,"given":"Themistoklis P.","family":"Sapsis","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,12,19]]},"reference":[{"key":"376_CR1","unstructured":"Creating a Disaster Resilient America: Grand Challenges in Science and Technology (National Academies Press, 2005)."},{"key":"376_CR2","doi-asserted-by":"crossref","unstructured":"Hansteen, O. E., Jostad, H. P. & Tjelta, T. I. Observed platform response to a \u201cmonster\u201d wave. in Field Measurements in Geomechanics 73\u201386 (Taylor & Francis, 2003).","DOI":"10.1201\/9781439833483.ch11"},{"key":"376_CR3","doi-asserted-by":"publisher","first-page":"1718","DOI":"10.1038\/s41598-022-05671-4","volume":"12","author":"J Gemmrich","year":"2022","unstructured":"Gemmrich, J. & Cicon, L. Generation mechanism and prediction of an observed extreme rogue wave. Sci. Rep. 12, 1718 (2022).","journal-title":"Sci. Rep."},{"key":"376_CR4","doi-asserted-by":"publisher","first-page":"85","DOI":"10.1146\/annurev-fluid-030420-032810","volume":"53","author":"TP Sapsis","year":"2021","unstructured":"Sapsis, T. P. Statistics of extreme events in fluid flows and waves. Annu. Rev. Fluid Mech. 53, 85\u2013111 (2021).","journal-title":"Annu. Rev. Fluid Mech."},{"key":"376_CR5","doi-asserted-by":"publisher","first-page":"564","DOI":"10.1137\/20M1347486","volume":"9","author":"A Blanchard","year":"2021","unstructured":"Blanchard, A. & Sapsis, T. Output-weighted optimal sampling for Bayesian experimental design and uncertainty quantification. SIAM\/ASA J. Uncertain. Quantif. 9, 564\u2013592 (2021).","journal-title":"SIAM\/ASA J. Uncertain. Quantif."},{"key":"376_CR6","doi-asserted-by":"publisher","first-page":"109901","DOI":"10.1016\/j.jcp.2020.109901","volume":"425","author":"A Blanchard","year":"2021","unstructured":"Blanchard, A. & Sapsis, T. P. Bayesian optimization with output-weighted optimal sampling. J. Comput. Phys. 425, 109901 (2021).","journal-title":"J. Comput. Phys."},{"key":"376_CR7","doi-asserted-by":"publisher","first-page":"218","DOI":"10.1038\/s42256-021-00302-5","volume":"3","author":"L Lu","year":"2021","unstructured":"Lu, L., Jin, P., Pang, G., Zhang, Z. & Karniadakis, G. E. Learning nonlinear operators via DeepONet based on the universal approximation theorem of operators. Nat. Mach. Intell. 3, 218\u2013229 (2021).","journal-title":"Nat. Mach. Intell."},{"key":"376_CR8","first-page":"263","volume":"1","author":"H Kahn","year":"1953","unstructured":"Kahn, H. & Marshall, A. W. Methods of reducing sample size in Monte Carlo computations. J. Op. Res. Soc. Am. 1, 263\u2013278 (1953).","journal-title":"J. Op. Res. Soc. Am."},{"key":"376_CR9","doi-asserted-by":"publisher","first-page":"721","DOI":"10.1061\/(ASCE)0733-9445(1983)109:3(721)","volume":"109","author":"M Shinozuka","year":"1983","unstructured":"Shinozuka, M. Basic analysis of structural safety. J. Struct. Eng. 109, 721\u2013740 (1983).","journal-title":"J. Struct. Eng."},{"key":"376_CR10","doi-asserted-by":"publisher","first-page":"1029","DOI":"10.1137\/18M1211003","volume":"7","author":"G Dematteis","year":"2019","unstructured":"Dematteis, G., Grafke, T. & Vanden-Eijnden, E. Extreme event quantification in dynamical systems with random components. SIAM\/ASA J. Uncertain. Quantif. 7, 1029\u20131059 (2019).","journal-title":"SIAM\/ASA J. Uncertain. Quantif."},{"key":"376_CR11","doi-asserted-by":"publisher","first-page":"818","DOI":"10.1137\/20M1344585","volume":"9","author":"F Uribe","year":"2021","unstructured":"Uribe, F., Papaioannou, I., Marzouk, Y. M. & Straub, D. Cross-entropy-based importance sampling with failure-informed dimension reduction for rare event simulation. SIAM\/ASA J. Uncertain. Quantif. 9, 818\u2013847 (2021).","journal-title":"SIAM\/ASA J. Uncertain. Quantif."},{"key":"376_CR12","unstructured":"Wahal, S. & Biros, G. BIMC: the Bayesian Inverse Monte Carlo method for goal-oriented uncertainty quantification. Part I. Preprint at https:\/\/arxiv.org\/abs\/1911.00619 (2019)."},{"key":"376_CR13","unstructured":"Gal, Y., Islam, R. & Ghahramani, Z. Deep Bayesian active learning with image data. In Proc. International Conference on Machine Learning (eds Precup, D. & Teh, Y. W.) 1183\u20131192 (PMLR, 2017)."},{"key":"376_CR14","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Lease, M. & Wallace, B. Active discriminative text representation learning. In Proc. AAAI Conference on Artificial Intelligence Vol. 31, 3386\u20133392 (AAAI, 2017).","DOI":"10.1609\/aaai.v31i1.10962"},{"key":"376_CR15","doi-asserted-by":"crossref","unstructured":"Aghdam, H. H., Gonzalez-Garcia, A., van de Weijer, J. & L\u00f3pez, A. M. Active learning for deep detection neural networks. In Proc. IEEE\/CVF International Conference on Computer Vision 3672\u20133680 (IEEE, 2019).","DOI":"10.1109\/ICCV.2019.00377"},{"key":"376_CR16","first-page":"1","volume":"54","author":"P Ren","year":"2021","unstructured":"Ren, P. et al. A survey of deep active learning. ACM Comput. Surveys 54, 1\u201340 (2021).","journal-title":"ACM Comput. Surveys"},{"key":"376_CR17","doi-asserted-by":"publisher","first-page":"106684","DOI":"10.1016\/j.ymssp.2020.106684","volume":"140","author":"Z Xiang","year":"2020","unstructured":"Xiang, Z., Chen, J., Bao, Y. & Li, H. An active learning method combining deep neural network and weighted sampling for structural reliability analysis. Mech. Syst. Signal Process. 140, 106684 (2020).","journal-title":"Mech. Syst. Signal Process."},{"key":"376_CR18","doi-asserted-by":"publisher","first-page":"B558","DOI":"10.1137\/21M1416758","volume":"44","author":"M Ehre","year":"2022","unstructured":"Ehre, M., Papaioannou, I., Sudret, B. & Straub, D. Sequential active learning of low-dimensional model representations for reliability analysis. SIAM J. Sci. Comput. 44, B558\u2013B584 (2022).","journal-title":"SIAM J. Sci. Comput."},{"key":"376_CR19","doi-asserted-by":"publisher","first-page":"145","DOI":"10.1016\/j.strusafe.2011.01.002","volume":"33","author":"B Echard","year":"2011","unstructured":"Echard, B., Gayton, N. & Lemaire, M. AK-MCS: an active learning reliability method combining Kriging and Monte Carlo simulation. Struct. Safety 33, 145\u2013154 (2011).","journal-title":"Struct. Safety"},{"key":"376_CR20","first-page":"1257","volume":"18","author":"E Snelson","year":"2006","unstructured":"Snelson, E. & Ghahramani, Z. Sparse Gaussian processes using pseudo-inputs. Adv. Neural Inf. Process. Syst. 18, 1257\u20131264 (2006).","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"376_CR21","unstructured":"Titsias, M. Variational learning of inducing variables in sparse Gaussian processes. In Proc. Artificial Intelligence and Statistics (eds van Dyk, D. & Welling, M.) 567\u2013574 (PMLR, 2009)."},{"key":"376_CR22","unstructured":"Pickering, E. & Sapsis, T. P. Structure and distribution metric for quantifying the quality of uncertainty: assessing Gaussian processes, deep neural nets and deep neural operators for regression. Preprint at https:\/\/arxiv.org\/abs\/2203.04515 (2022)."},{"key":"376_CR23","unstructured":"Wilson, A. G. & Izmailov, P. Bayesian deep learning and a probabilistic perspective of generalization. In Proc. 34th International Conference on Neural Information Processing Systems (eds Larochelle, H. et al.) 4697\u20134708 (Curran Associates Inc., 2020)."},{"key":"376_CR24","doi-asserted-by":"publisher","first-page":"20190834","DOI":"10.1098\/rspa.2019.0834","volume":"476","author":"TP Sapsis","year":"2020","unstructured":"Sapsis, T. P. Output-weighted optimal sampling for Bayesian regression and rare event statistics using few samples. Proc. R. Soc. A 476, 20190834 (2020).","journal-title":"Proc. R. Soc. A"},{"key":"376_CR25","doi-asserted-by":"publisher","first-page":"11138","DOI":"10.1073\/pnas.1813263115","volume":"115","author":"MA Mohamad","year":"2018","unstructured":"Mohamad, M. A. & Sapsis, T. P. Sequential sampling strategy for extreme event statistics in nonlinear dynamical systems. Proc. Natl Acad. Sci. USA 115, 11138\u201311143 (2018).","journal-title":"Proc. Natl Acad. Sci. USA"},{"key":"376_CR26","doi-asserted-by":"publisher","first-page":"20210197","DOI":"10.1098\/rsta.2021.0197","volume":"380","author":"TP Sapsis","year":"2022","unstructured":"Sapsis, T. P. & Blanchard, A. Optimal criteria and their asymptotic form for data selection in data-driven reduced-order modelling with Gaussian process regression. Philos. Trans. R. Soc. A 380, 20210197 (2022).","journal-title":"Philos. Trans. R. Soc. A"},{"key":"376_CR27","doi-asserted-by":"publisher","first-page":"700","DOI":"10.1098\/rspa.1927.0118","volume":"115","author":"WO Kermack","year":"1927","unstructured":"Kermack, W. O. & McKendrick, A. G. A contribution to the mathematical theory of epidemics. Proc. R. Soc. Lond. A 115, 700\u2013721 (1927).","journal-title":"Proc. R. Soc. Lond. A"},{"key":"376_CR28","doi-asserted-by":"publisher","first-page":"361","DOI":"10.1038\/280361a0","volume":"280","author":"RM Anderson","year":"1979","unstructured":"Anderson, R. M. & May, R. M. Population biology of infectious diseases: Part I. Nature 280, 361\u2013367 (1979).","journal-title":"Nature"},{"key":"376_CR29","doi-asserted-by":"publisher","first-page":"9","DOI":"10.1007\/BF02679124","volume":"7","author":"AJ Majda","year":"1997","unstructured":"Majda, A. J., McLaughlin, D. W. & Tabak, E. G. A one-dimensional model for dispersive wave turbulence. J. Nonlinear Sci. 7, 9\u201344 (1997).","journal-title":"J. Nonlinear Sci."},{"key":"376_CR30","doi-asserted-by":"publisher","first-page":"14216","DOI":"10.1073\/pnas.96.25.14216","volume":"96","author":"D Cai","year":"1999","unstructured":"Cai, D., Majda, A. J., McLaughlin, D. W. & Tabak, E. G. Spectral bifurcations in dispersive wave turbulence. Proc. Natl Acad. Sci. USA 96, 14216\u201314221 (1999).","journal-title":"Proc. Natl Acad. Sci. USA"},{"key":"376_CR31","doi-asserted-by":"publisher","first-page":"573","DOI":"10.1016\/S0167-2789(01)00194-4","volume":"152-153","author":"VE Zakharov","year":"2001","unstructured":"Zakharov, V. E., Guyenne, P., Pushkarev, A. N. & Dias, F. Wave turbulence in one-dimensional models. Phys. D: Nonlinear Phenom. 152-153, 573\u2013619 (2001).","journal-title":"Phys. D: Nonlinear Phenom."},{"key":"376_CR32","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.physrep.2004.04.002","volume":"398","author":"VE Zakharov","year":"2004","unstructured":"Zakharov, V. E., Dias, F. & Pushkarev, A. One-dimensional wave turbulence. Phys. Rep. 398, 1\u201365 (2004).","journal-title":"Phys. Rep."},{"key":"376_CR33","doi-asserted-by":"publisher","first-page":"55","DOI":"10.1016\/j.physd.2013.01.003","volume":"248","author":"A Pushkarev","year":"2013","unstructured":"Pushkarev, A. & Zakharov, V. E. Quasibreathers in the MMT model. Phys. D: Nonlinear Phenom. 248, 55\u201361 (2013).","journal-title":"Phys. D: Nonlinear Phenom."},{"key":"376_CR34","doi-asserted-by":"publisher","first-page":"48","DOI":"10.1016\/j.physd.2014.04.012","volume":"280","author":"W Cousins","year":"2014","unstructured":"Cousins, W. & Sapsis, T. P. Quantification and prediction of extreme events in a one-dimensional nonlinear dispersive wave model. Phys. D: Nonlinear Phenom. 280, 48\u201358 (2014).","journal-title":"Phys. D: Nonlinear Phenom."},{"key":"376_CR35","unstructured":"Chapelle, O. & Li, L. An empirical evaluation of Thompson sampling. In Proc. 24th International Conference on Neural Information Processing Systems (eds Shawe-Taylor, J. et al.) 2249\u20132257 (Curran Associates Inc., 2011)."},{"key":"376_CR36","doi-asserted-by":"publisher","first-page":"124003","DOI":"10.1088\/1742-5468\/ac3a74","volume":"2021","author":"P Nakkiran","year":"2021","unstructured":"Nakkiran, P. et al. Deep double descent: where bigger models and more data hurt. J. Stat. Mech. 2021, 124003 (2021).","journal-title":"J. Stat. Mech."},{"key":"376_CR37","unstructured":"Pickering, E. & Sapsis, T. P. Information FOMO: the unhealthy fear of missing out on information. A method for removing misleading data for healthier models. Preprint at https:\/\/arxiv.org\/abs\/2208.13080 (2022)."},{"key":"376_CR38","unstructured":"Sapsis, T., Pipiras, V., Weems, K. & Belenky, V. On extreme value properties of vertical bending moment. In Proc. 33rd Symposium on Naval Hydrodynamics Osaka, Japan (Virtual) (2020)."},{"key":"376_CR39","unstructured":"Sapsis, T. P., Belenky, V., Weems, K. & Pipiras, V. Extreme properties of impact-induced vertical bending moments. In Proc. 1st International Conference on the Stability and Safety of Ships and Ocean Vehicles (2021)."},{"key":"376_CR40","unstructured":"Belenky, V., Weems, K., Sapsis, T. P. & Pipiras, V. Influence of deck submergence events on extreme properties of wave-induced vertical bending moment. In Proc. 1st International Conference on the Stability and Safety of Ships and Ocean Vehicles (2021)."},{"key":"376_CR41","doi-asserted-by":"publisher","first-page":"1193","DOI":"10.1016\/j.scriptamat.2005.07.015","volume":"53","author":"S Serebrinsky","year":"2005","unstructured":"Serebrinsky, S. & Ortiz, M. A hysteretic cohesive-law model of fatigue-crack nucleation. Scripta Mater. 53, 1193\u20131196 (2005).","journal-title":"Scripta Mater."},{"key":"376_CR42","doi-asserted-by":"crossref","unstructured":"Khan, R. A. & Ahmad, S. Dynamic response and fatigue reliability analysis of marine riser under random loads. In Proc. Petroleum Technology Symposium of International Conference on Offshore Mechanics and Arctic Engineering Vol. 2, 183\u2013191 (ASME, 2007).","DOI":"10.1115\/OMAE2007-29235"},{"key":"376_CR43","doi-asserted-by":"crossref","unstructured":"Chasparis, F. et al. Lock-in, transient and chaotic response in riser VIV. In Proc. International Conference on Offshore Mechanics and Arctic Engineering, Vol. 5, 479\u2013485 (ASME, 2009).","DOI":"10.1115\/OMAE2009-79444"},{"key":"376_CR44","unstructured":"Lin, W.-M., Zhang, S. & Weems, K. M. Numerical simulations of surface effect ship in waves. In Proc. 2010 Conference on Grand Challenges in Modeling and Simulation 414\u2013421 (Society for Modeling and Simulation International, 2010)."},{"key":"376_CR45","unstructured":"Li, Z. et al. Fourier neural operator for parametric partial differential equations. In Proc. International Conference on Learning Representations (ICLR, 2021); https:\/\/openreview.net\/forum?id=c8P9NQVtmnO"},{"key":"376_CR46","doi-asserted-by":"publisher","first-page":"20210781","DOI":"10.1098\/rspa.2021.0781","volume":"478","author":"Y Yang","year":"2022","unstructured":"Yang, Y., Blanchard, A., Sapsis, T. P. & Perdikaris, P. Output-weighted sampling for multi-armed bandits with extreme payoffs. Proc. R. Soc. A 478, 20210781 (2022).","journal-title":"Proc. R. Soc. A"},{"key":"376_CR47","doi-asserted-by":"publisher","first-page":"993","DOI":"10.1109\/34.58871","volume":"12","author":"LK Hansen","year":"1990","unstructured":"Hansen, L. K. & Salamon, P. Neural network ensembles. IEEE Trans. Pattern Anal. Mach. Intell. 12, 993\u20131001 (1990).","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"376_CR48","unstructured":"Lakshminarayanan, B., Pritzel, A. & Blundell, C. Simple and scalable predictive uncertainty estimation using deep ensembles. In Proc. 31st International Conference on Neural Information Processing Systems (eds Guyon, I. et al.) 6405\u20136416 (Curran Associates Inc., 2017)."},{"key":"376_CR49","doi-asserted-by":"crossref","unstructured":"Gustafsson, F. K., Danelljan, M. & Schon, T. B. Evaluating scalable Bayesian deep learning methods for robust computer vision. In Proc. IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops 318\u2013319 (IEEE, 2020).","DOI":"10.1109\/CVPRW50498.2020.00167"},{"key":"376_CR50","unstructured":"Loshchilov, I. & Hutter, F. SGDR: stochastic gradient descent with warm restarts. In Proc. International Conference on Learning Representations (ICLR, 2017); https:\/\/openreview.net\/forum?id=Skq89Scxx"},{"key":"376_CR51","unstructured":"Huang, G. et al. Snapshot ensembles: train 1, get M for free. In Proc. International Conference on Learning Representations (ICLR, 2017); https:\/\/openreview.net\/forum?id=BJYwwY9ll"},{"key":"376_CR52","unstructured":"Smith, L. N. No more pesky learning rate guessing games. Preprint at https:\/\/arxiv.org\/abs\/1506.01186 (2015)."},{"key":"376_CR53","doi-asserted-by":"publisher","first-page":"130","DOI":"10.1198\/TECH.2009.0015","volume":"51","author":"RB Gramacy","year":"2009","unstructured":"Gramacy, R. B. & Lee, H. K. H. Adaptive design and analysis of supercomputer experiments. Technometrics 51, 130\u2013145 (2009).","journal-title":"Technometrics"},{"key":"376_CR54","doi-asserted-by":"publisher","first-page":"285","DOI":"10.1093\/biomet\/25.3-4.285","volume":"25","author":"WR Thompson","year":"1933","unstructured":"Thompson, W. R. On the likelihood that one unknown probability exceeds another in view of the evidence of two samples. Biometrika 25, 285\u2013294 (1933).","journal-title":"Biometrika"},{"key":"376_CR55","doi-asserted-by":"publisher","unstructured":"Pickering, E. dnosearch_nature_cs_data (Zenodo, 2022); https:\/\/doi.org\/10.5281\/zenodo.7314144","DOI":"10.5281\/zenodo.7314144"},{"key":"376_CR56","doi-asserted-by":"publisher","unstructured":"Pickering, E. dnosearch (Zenodo, 2022); https:\/\/doi.org\/10.5281\/zenodo.7312058","DOI":"10.5281\/zenodo.7312058"}],"container-title":["Nature Computational Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s43588-022-00376-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s43588-022-00376-0","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s43588-022-00376-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,19]],"date-time":"2022-12-19T12:38:12Z","timestamp":1671453492000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s43588-022-00376-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,12,19]]},"references-count":56,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2022,12]]}},"alternative-id":["376"],"URL":"https:\/\/doi.org\/10.1038\/s43588-022-00376-0","relation":{"has-preprint":[{"id-type":"doi","id":"10.21203\/rs.3.rs-1527132\/v1","asserted-by":"object"}]},"ISSN":["2662-8457"],"issn-type":[{"value":"2662-8457","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,12,19]]},"assertion":[{"value":"5 April 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 November 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 December 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare no competing interests.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}]}}