{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,3]],"date-time":"2026-07-03T02:07:16Z","timestamp":1783044436186,"version":"3.54.6"},"update-to":[{"DOI":"10.1371\/journal.pcbi.1011047","type":"new_version","label":"New version","source":"publisher","updated":{"date-parts":[[2023,4,27]],"date-time":"2023-04-27T00:00:00Z","timestamp":1682553600000}}],"reference-count":67,"publisher":"Public Library of Science (PLoS)","issue":"4","license":[{"start":{"date-parts":[[2023,4,17]],"date-time":"2023-04-17T00:00:00Z","timestamp":1681689600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["www.ploscompbiol.org"],"crossmark-restriction":false},"short-container-title":["PLoS Comput Biol"],"abstract":"<jats:p>Making no use of physical laws or co-evolutionary information, <jats:italic>de novo<\/jats:italic> deep learning (DL) models for RNA secondary structure prediction have achieved far superior performances than traditional algorithms. However, their statistical underpinning raises the crucial question of generalizability. We present a quantitative study of the performance and generalizability of a series of <jats:italic>de novo<\/jats:italic> DL models, with a minimal two-module architecture and no post-processing, under varied similarities between seen and unseen sequences. Our models demonstrate excellent expressive capacities and outperform existing methods on common benchmark datasets. However, model generalizability, i.e., the performance gap between the seen and unseen sets, degrades rapidly as the sequence similarity decreases. The same trends are observed from several recent DL and machine learning models. And an inverse correlation between performance and generalizability is revealed collectively across all learning-based models with wide-ranging architectures and sizes. We further quantitate how generalizability depends on sequence and structure identity scores via pairwise alignment, providing unique quantitative insights into the limitations of statistical learning. Generalizability thus poses a major hurdle for deploying <jats:italic>de novo<\/jats:italic> DL models in practice and various pathways for future advances are discussed.<\/jats:p>","DOI":"10.1371\/journal.pcbi.1011047","type":"journal-article","created":{"date-parts":[[2023,4,17]],"date-time":"2023-04-17T17:34:21Z","timestamp":1681752861000},"page":"e1011047","update-policy":"https:\/\/doi.org\/10.1371\/journal.pcbi.corrections_policy","source":"Crossref","is-referenced-by-count":20,"title":["Sequence similarity governs generalizability of de novo deep learning models for RNA secondary structure prediction"],"prefix":"10.1371","volume":"19","author":[{"given":"Xiangyun","family":"Qiu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2023,4,17]]},"reference":[{"issue":"3","key":"pcbi.1011047.ref001","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1017\/S0033583500003620","article-title":"RNA secondary structure: physical and computational aspects","volume":"33","author":"PG Higgs","year":"2000","journal-title":"Q Rev Biophys"},{"key":"pcbi.1011047.ref002","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1016\/j.jbiotec.2017.07.007","article-title":"Recent advances in RNA folding","volume":"261","author":"J Fallmann","year":"2017","journal-title":"J Biotechnol"},{"key":"pcbi.1011047.ref003","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1146\/annurev.biophys.37.032807.125957","article-title":"RNA folding: conformational statistics, folding kinetics, and ion electrostatics.","volume":"37","author":"SJ Chen","year":"2008","journal-title":"Annu Rev Biophys."},{"issue":"1","key":"pcbi.1011047.ref004","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1016\/j.cell.2014.03.008","article-title":"The noncoding RNA revolution-trashing old rules to forge new ones","volume":"157","author":"TR Cech","year":"2014","journal-title":"Cell"},{"issue":"4","key":"pcbi.1011047.ref005","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1016\/j.tig.2014.01.004","article-title":"The four dimensions of noncoding RNA conservation","volume":"30","author":"S. 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