{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T21:57:44Z","timestamp":1774648664979,"version":"3.50.1"},"reference-count":62,"publisher":"Oxford University Press (OUP)","issue":"21","license":[{"start":{"date-parts":[[2020,7,23]],"date-time":"2020-07-23T00:00:00Z","timestamp":1595462400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61822306"],"award-info":[{"award-number":["61822306"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61672184"],"award-info":[{"award-number":["61672184"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61702134"],"award-info":[{"award-number":["61702134"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61861146002"],"award-info":[{"award-number":["61861146002"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61732012"],"award-info":[{"award-number":["61732012"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004826","name":"Beijing Natural Science Foundation","doi-asserted-by":"publisher","award":["JQ19019"],"award-info":[{"award-number":["JQ19019"]}],"id":[{"id":"10.13039\/501100004826","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100010261","name":"Fok Ying-Tung Education Foundation","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100010261","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Higher Education Institutions of China","award":["161063"],"award-info":[{"award-number":["161063"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,1,29]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>Related to many important biological functions, intrinsically disordered regions (IDRs) are widely distributed in proteins. Accurate prediction of IDRs is critical for the protein structure and function analysis. However, the existing computational methods construct the predictive models solely in the sequence space, failing to convert the sequence space into the \u2018semantic space\u2019 to reflect the structure characteristics of proteins. Furthermore, although the length-dependent predictors showed promising results, new fusion strategies should be explored to improve their predictive performance and the generalization.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>In this study, we applied the Sequence to Sequence Learning (Seq2Seq) derived from natural language processing (NLP) to map protein sequences to \u2018semantic space\u2019 to reflect the structure patterns with the help of predicted residue\u2013residue contacts (CCMs) and other sequence-based features. Furthermore, the Attention mechanism was used to capture the global associations between all residue pairs in the proteins. Three length-dependent predictors were constructed: IDP-Seq2Seq-L for long disordered region prediction, IDP-Seq2Seq-S for short disordered region prediction and IDP-Seq2Seq-G for both long and short disordered region predictions. Finally, these three predictors were fused into one predictor called IDP-Seq2Seq to improve the discriminative power and generalization. Experimental results on four independent test datasets and the CASP test dataset showed that IDP-Seq2Seq is insensitive with the ratios of long and short disordered regions and outperforms other competing methods.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>For the convenience of most experimental scientists, a user-friendly and publicly accessible web-server for the powerful new predictor has been established at http:\/\/bliulab.net\/IDP-Seq2Seq\/. It is anticipated that IDP-Seq2Seq will become a very useful tool for identification of IDRs.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Supplementary information<\/jats:title>\n                  <jats:p>Supplementary data are available at Bioinformatics online.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btaa667","type":"journal-article","created":{"date-parts":[[2020,7,17]],"date-time":"2020-07-17T11:10:16Z","timestamp":1594984216000},"page":"5177-5186","source":"Crossref","is-referenced-by-count":146,"title":["IDP-Seq2Seq: identification of intrinsically disordered regions based on sequence to sequence learning"],"prefix":"10.1093","volume":"36","author":[{"given":"Yi-Jun","family":"Tang","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology, Beijing Institute of Technology , Beijing 100081, China"}]},{"given":"Yi-He","family":"Pang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Beijing Institute of Technology , Beijing 100081, China"}]},{"given":"Bin","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Beijing Institute of Technology , Beijing 100081, China"},{"name":"Advanced Research Institute of Multidisciplinary Science, Beijing Institute of Technology , Beijing 100081, China"}]}],"member":"286","published-online":{"date-parts":[[2020,7,23]]},"reference":[{"key":"2023062408070730800_btaa667-B1","doi-asserted-by":"crossref","first-page":"753","DOI":"10.1002\/prot.20176","article-title":"Accurate prediction of solvent accessibility using neural networks\u2013based regression","volume":"56","author":"Adamczak","year":"2004","journal-title":"Proteins"},{"key":"2023062408070730800_btaa667-B2","doi-asserted-by":"crossref","first-page":"3389","DOI":"10.1093\/nar\/25.17.3389","article-title":"Gapped BLAST and PSI-BLAST: a new generation of protein database search programs","volume":"25","author":"Altschul","year":"1997","journal-title":"Nucleic Acids Res"},{"key":"2023062408070730800_btaa667-B3","author":"Bahdanau","year":"2015"},{"key":"2023062408070730800_btaa667-B4","author":"Baruh"},{"key":"2023062408070730800_btaa667-B5","doi-asserted-by":"crossref","first-page":"3473","DOI":"10.1093\/bioinformatics\/btx429","article-title":"ProtDec-LTR2.0: an improved method for protein remote homology detection by combining pseudo protein and supervised Learning to Rank","volume":"33","author":"Chen","year":"2017","journal-title":"Bioinformatics"},{"key":"2023062408070730800_btaa667-B6","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1007\/s10618-005-0001-y","article-title":"Accurate prediction of protein disordered regions by mining protein structure data","volume":"11","author":"Cheng","year":"2005","journal-title":"Data Min. Knowl. Discov"},{"key":"2023062408070730800_btaa667-B7","first-page":"1724","author":"Cho","year":"2014"},{"key":"2023062408070730800_btaa667-B8","author":"Chung","year":"2014"},{"key":"2023062408070730800_btaa667-B9","doi-asserted-by":"crossref","first-page":"114","DOI":"10.1039\/C1MB05207A","article-title":"A comprehensive overview of computational protein disorder prediction methods","volume":"8","author":"Deng","year":"2012","journal-title":"Mol. bioSyst"},{"key":"2023062408070730800_btaa667-B10","first-page":"33","author":"Dong","year":"2016"},{"key":"2023062408070730800_btaa667-B11","doi-asserted-by":"crossref","first-page":"3433","DOI":"10.1093\/bioinformatics\/bti541","article-title":"IUPred: web server for the prediction of intrinsically unstructured regions of proteins based on estimated energy content","volume":"21","author":"Dosztanyi","year":"2005","journal-title":"Bioinformatics"},{"key":"2023062408070730800_btaa667-B12","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1038\/nrm1589","article-title":"Intrinsically unstructured proteins and their functions","volume":"6","author":"Dyson","year":"2005","journal-title":"Nat. Rev. Mol. Cell Biol"},{"key":"2023062408070730800_btaa667-B13","doi-asserted-by":"crossref","DOI":"10.1142\/S0219720012710011","article-title":"A decade after the first full human genome sequencing: when will we understand our own genome?","volume":"10","author":"Eisenhaber","year":"2012","journal-title":"J. Bioinf. Comput. Biol"},{"key":"2023062408070730800_btaa667-B15","doi-asserted-by":"crossref","first-page":"685","DOI":"10.1093\/bioinformatics\/btw678","article-title":"Improving protein disorder prediction by deep bidirectional long short-term memory recurrent neural networks","volume":"33","author":"Hanson","year":"2017","journal-title":"Bioinformatics"},{"key":"2023062408070730800_btaa667-B16","doi-asserted-by":"crossref","first-page":"2046","DOI":"10.1093\/bioinformatics\/btm302","article-title":"POODLE-L: a two-level SVM prediction system for reliably predicting long disordered regions","volume":"23","author":"Hirose","year":"2007","journal-title":"Bioinformatics"},{"key":"2023062408070730800_btaa667-B17","doi-asserted-by":"crossref","first-page":"423","DOI":"10.1093\/bioinformatics\/14.5.423","article-title":"Removing near-neighbour redundancy from large protein sequence collections","volume":"14","author":"Holm","year":"1998","journal-title":"Bioinformatics"},{"key":"2023062408070730800_btaa667-B18","first-page":"4099","author":"Hu","year":"2018"},{"key":"2023062408070730800_btaa667-B19","doi-asserted-by":"crossref","first-page":"573","DOI":"10.1016\/S0022-2836(02)00969-5","article-title":"Intrinsic disorder in cell-signaling and cancer-associated proteins","volume":"323","author":"Iakoucheva","year":"2002","journal-title":"J. Mol. Biol"},{"key":"2023062408070730800_btaa667-B20","doi-asserted-by":"crossref","first-page":"573","DOI":"10.1016\/S0022-2836(02)00969-5","article-title":"Intrinsic disorder in cell-signaling and cancer-associated proteins","volume":"323","author":"Iakoucheva","year":"2002","journal-title":"J. Mol. Biol"},{"key":"2023062408070730800_btaa667-B21","doi-asserted-by":"crossref","first-page":"857","DOI":"10.1093\/bioinformatics\/btu744","article-title":"DISOPRED3: precise disordered region predictions with annotated protein-binding activity","volume":"31","author":"Jones","year":"2015","journal-title":"Bioinformatics"},{"key":"2023062408070730800_btaa667-B22","volume-title":"A Method for Stochastic Optimization","author":"Kingma","year":"2015"},{"key":"2023062408070730800_btaa667-B23","doi-asserted-by":"crossref","first-page":"472","DOI":"10.1093\/bioinformatics\/btt709","article-title":"Combining evolutionary information extracted from frequency profiles with sequence-based kernels for protein remote homology detection","volume":"30","author":"Liu","year":"2014","journal-title":"Bioinformatics"},{"key":"2023062408070730800_btaa667-B24","doi-asserted-by":"crossref","first-page":"2483","DOI":"10.3390\/ijms19092483","article-title":"IDP(-)CRF: intrinsically disordered protein\/region identification based on conditional random fields","volume":"19","author":"Liu","year":"2018","journal-title":"Int. J. Mol. Sci"},{"key":"2023062408070730800_btaa667-B26","doi-asserted-by":"crossref","first-page":"396","DOI":"10.1016\/j.omtn.2019.06.004","article-title":"Identification of intrinsically disordered proteins and regions by length-dependent predictors based on conditional random fields","volume":"17","author":"Liu","year":"2019","journal-title":"Mol. Therapy Nucleic Acids"},{"key":"2023062408070730800_btaa667-B27","article-title":"RFPR-IDP: reduce the false positive rates for intrinsically disordered protein and region prediction by incorporating both fully ordered proteins and disordered proteins","author":"Liu","year":"2020","journal-title":"Brief. Bioinf"},{"key":"2023062408070730800_btaa667-B28","doi-asserted-by":"crossref","first-page":"1798","DOI":"10.1093\/bioinformatics\/btn326","article-title":"Intrinsic disorder prediction from the analysis of multiple protein fold recognition models","volume":"24","author":"McGuffin","year":"2008","journal-title":"Bioinformatics"},{"key":"2023062408070730800_btaa667-B29","doi-asserted-by":"crossref","first-page":"360","DOI":"10.1007\/s008940100038","article-title":"Generation and evaluation of dimension-reduced amino acid parameter representations by artificial neural networks","volume":"7","author":"Meiler","year":"2001","journal-title":"J. Mol. Model"},{"key":"2023062408070730800_btaa667-B30","doi-asserted-by":"crossref","first-page":"i489","DOI":"10.1093\/bioinformatics\/btq373","article-title":"Improved sequence-based prediction of disordered regions with multilayer fusion of multiple information sources","volume":"26","author":"Mizianty","year":"2010","journal-title":"Bioinformatics"},{"key":"2023062408070730800_btaa667-B31","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1002\/prot.23161","article-title":"Evaluation of disorder predictions in CASP9","volume":"79","author":"Monastyrskyy","year":"2011","journal-title":"Proteins"},{"key":"2023062408070730800_btaa667-B32","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1002\/prot.24391","article-title":"Assessment of protein disorder region predictions in CASP10","volume":"82","author":"Monastyrskyy","year":"2014","journal-title":"Proteins"},{"key":"2023062408070730800_btaa667-B33","doi-asserted-by":"crossref","first-page":"176","DOI":"10.1002\/prot.20735","article-title":"Exploiting heterogeneous sequence properties improves prediction of protein disorder","volume":"61","author":"Obradovic","year":"2005","journal-title":"Proteins"},{"key":"2023062408070730800_btaa667-B34","doi-asserted-by":"crossref","first-page":"179143","DOI":"10.1109\/ACCESS.2019.2949086","article-title":"A deep neural network model for joint entity and relation extraction","volume":"7","author":"Pang","year":"2019","journal-title":"IEEE Access"},{"key":"2023062408070730800_btaa667-B35","doi-asserted-by":"crossref","first-page":"208","DOI":"10.1186\/1471-2105-7-208","article-title":"Length-dependent prediction of protein intrinsic disorder","volume":"7","author":"Peng","year":"2006","journal-title":"BMC Bioinformatics"},{"key":"2023062408070730800_btaa667-B36","doi-asserted-by":"crossref","first-page":"6","DOI":"10.2174\/138920312799277938","article-title":"Comprehensive comparative assessment of in-silico predictors of disordered regions","volume":"13","author":"Peng","year":"2012","journal-title":"Curr. Protein Peptide Sci"},{"key":"2023062408070730800_btaa667-B37","article-title":"DisProt 7.0: a major update of the database of disordered proteins","volume":"45","author":"Piovesan","year":"2016","journal-title":"Nucleic Acids Res"},{"key":"2023062408070730800_btaa667-B38","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1038\/nmeth.1818","article-title":"HHblits: lightning-fast iterative protein sequence searching by HMM-HMM alignment","volume":"9","author":"Remmert","year":"2012","journal-title":"Nat. Methods"},{"key":"2023062408070730800_btaa667-B39","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1002\/1097-0134(20010101)42:1<38::AID-PROT50>3.0.CO;2-3","article-title":"Sequence complexity of disordered protein","volume":"42","author":"Romero","year":"2001","journal-title":"Proteins"},{"key":"2023062408070730800_btaa667-B40","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1016\/j.gde.2015.08.010","article-title":"The language of the protein universe","volume":"35","author":"Scaiewicz","year":"2015","journal-title":"Curr. Opin. Genet. Dev"},{"key":"2023062408070730800_btaa667-B41","doi-asserted-by":"crossref","first-page":"891","DOI":"10.1093\/bioinformatics\/btl032","article-title":"PROFbval: predict flexible and rigid residues in proteins","volume":"22","author":"Schlessinger","year":"2006","journal-title":"Bioinformatics"},{"key":"2023062408070730800_btaa667-B42","doi-asserted-by":"crossref","first-page":"e140","DOI":"10.1371\/journal.pcbi.0030140","article-title":"Natively unstructured loops differ from other loops","volume":"3","author":"Schlessinger","year":"2007","journal-title":"PLoS Comput. Biol"},{"key":"2023062408070730800_btaa667-B43","doi-asserted-by":"crossref","first-page":"2376","DOI":"10.1093\/bioinformatics\/btm349","article-title":"Natively unstructured regions in proteins identified from contact predictions","volume":"23","author":"Schlessinger","year":"2007","journal-title":"Bioinformatics"},{"key":"2023062408070730800_btaa667-B44","doi-asserted-by":"crossref","first-page":"e4433","DOI":"10.1371\/journal.pone.0004433","article-title":"Improved disorder prediction by combination of orthogonal approaches","volume":"4","author":"Schlessinger","year":"2009","journal-title":"PLoS One"},{"key":"2023062408070730800_btaa667-B45","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1038\/nature01255","article-title":"The language of genes","volume":"420","author":"Searls","year":"2002","journal-title":"Nature"},{"key":"2023062408070730800_btaa667-B46","doi-asserted-by":"crossref","first-page":"3128","DOI":"10.1093\/bioinformatics\/btu500","article-title":"CCMpred\u2013fast and precise prediction of protein residue-residue contacts from correlated mutations","volume":"30","author":"Seemayer","year":"2014","journal-title":"Bioinformatics"},{"key":"2023062408070730800_btaa667-B47","doi-asserted-by":"crossref","first-page":"1850","DOI":"10.1093\/bioinformatics\/bty032","article-title":"OPAL: prediction of MoRF regions in intrinsically disordered protein sequences","volume":"34","author":"Sharma","year":"2018","journal-title":"Bioinformatics"},{"key":"2023062408070730800_btaa667-B48","doi-asserted-by":"crossref","first-page":"e1800058","DOI":"10.1002\/pmic.201800058","article-title":"OPAL+: length-specific MoRF prediction in intrinsically disordered protein sequences","volume":"19","author":"Sharma","year":"2019","journal-title":"Proteomics"},{"key":"2023062408070730800_btaa667-B49","doi-asserted-by":"crossref","first-page":"2337","DOI":"10.1093\/bioinformatics\/btm330","article-title":"POODLE-S: web application for predicting protein disorder by using physicochemical features and reduced amino acid set of a position-specific scoring matrix","volume":"23","author":"Shimizu","year":"2007","journal-title":"Bioinformatics"},{"key":"2023062408070730800_btaa667-B50","first-page":"433"},{"key":"2023062408070730800_btaa667-B51","doi-asserted-by":"crossref","first-page":"S15","DOI":"10.1186\/1471-2164-11-S1-S15","article-title":"Parameterization of disorder predictors for large-scale applications requiring high specificity by using an extended benchmark dataset","volume":"11","author":"Sirota","year":"2010","journal-title":"BMC Genomics"},{"key":"2023062408070730800_btaa667-B52","author":"Sutskever","year":"2014"},{"key":"2023062408070730800_btaa667-B53","doi-asserted-by":"crossref","first-page":"215","DOI":"10.1146\/annurev.biophys.37.032807.125924","article-title":"Intrinsically disordered proteins in human diseases: introducing the D2 concept","volume":"37","author":"Uversky","year":"2008","journal-title":"Annu. Rev. Biophys"},{"key":"2023062408070730800_btaa667-B54","doi-asserted-by":"crossref","first-page":"S7","DOI":"10.1186\/1471-2164-10-S1-S7","article-title":"Unfoldomics of human diseases: linking protein intrinsic disorder with diseases","volume":"10","author":"Uversky","year":"2009","journal-title":"BMC Genomics"},{"key":"2023062408070730800_btaa667-B55","first-page":"2773","volume-title":"Advances in neural information processing systems","author":"Vinyals","year":"2015"},{"key":"2023062408070730800_btaa667-B56","doi-asserted-by":"crossref","first-page":"355","DOI":"10.1089\/cmb.2005.12.355","article-title":"Linear regression models for solvent accessibility prediction in proteins","volume":"12","author":"Wagner","year":"2005","journal-title":"J. Comput. Biol. J. Comput. Mol. Cell Biol"},{"key":"2023062408070730800_btaa667-B57","doi-asserted-by":"crossref","first-page":"i672","DOI":"10.1093\/bioinformatics\/btw446","article-title":"AUCpreD: proteome-level protein disorder prediction by AUC-maximized deep convolutional neural fields","volume":"32","author":"Wang","year":"2016","journal-title":"Bioinformatics"},{"key":"2023062408070730800_btaa667-B58","doi-asserted-by":"crossref","first-page":"635","DOI":"10.1016\/j.jmb.2004.02.002","article-title":"Prediction and functional analysis of native disorder in proteins from the three kingdoms of life","volume":"337","author":"Ward","year":"2004","journal-title":"J. Mol. Biol"},{"key":"2023062408070730800_btaa667-B59","doi-asserted-by":"crossref","first-page":"996","DOI":"10.1016\/j.bbapap.2010.01.011","article-title":"PONDR-FIT: a meta-predictor of intrinsically disordered amino acids","volume":"1804","author":"Xue","year":"2010","journal-title":"Biochim. Biophys. Acta"},{"key":"2023062408070730800_btaa667-B60","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1007\/978-1-4939-6406-2_6","article-title":"SPIDER2: a package to predict secondary structure, accessible surface area, and main-chain torsional angles by deep neural networks","volume":"1484","author":"Yang","year":"2017","journal-title":"Methods Mol. Biol"},{"key":"2023062408070730800_btaa667-B61","doi-asserted-by":"crossref","first-page":"3369","DOI":"10.1093\/bioinformatics\/bti534","article-title":"RONN: the bio-basis function neural network technique applied to the detection of natively disordered regions in proteins","volume":"21","author":"Yang","year":"2005","journal-title":"Bioinformatics"},{"key":"2023062408070730800_btaa667-B62","doi-asserted-by":"crossref","first-page":"799","DOI":"10.1080\/073911012010525022","article-title":"SPINE-D: accurate prediction of short and long disordered regions by a single neural-network based method","volume":"29","author":"Zhang","year":"2012","journal-title":"J. Biomol. Struct. Dyn"},{"key":"2023062408070730800_btaa667-B63","doi-asserted-by":"crossref","first-page":"289","DOI":"10.2174\/157016461104150121115154","article-title":"Exploratory predicting protein folding model with random forest and hybrid features","volume":"11","author":"Zhao","year":"2015","journal-title":"Curr. Proteomics"},{"key":"2023062408070730800_btaa667-B64","first-page":"1","article-title":"Sequence clustering in bioinformatics: an empirical study","volume":"21","author":"Zou","year":"2020","journal-title":"Brief. Bioinf"}],"container-title":["Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/academic.oup.com\/bioinformatics\/advance-article-pdf\/doi\/10.1093\/bioinformatics\/btaa667\/33861603\/btaa667.pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/36\/21\/5177\/50692705\/btaa667.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/36\/21\/5177\/50692705\/btaa667.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,6,24]],"date-time":"2023-06-24T23:30:47Z","timestamp":1687649447000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article\/36\/21\/5177\/5875603"}},"subtitle":[],"editor":[{"given":"Arne","family":"Elofsson","sequence":"additional","affiliation":[]}],"short-title":[],"issued":{"date-parts":[[2020,7,23]]},"references-count":62,"journal-issue":{"issue":"21","published-print":{"date-parts":[[2021,1,29]]}},"URL":"https:\/\/doi.org\/10.1093\/bioinformatics\/btaa667","relation":{},"ISSN":["1367-4803","1367-4811"],"issn-type":[{"value":"1367-4803","type":"print"},{"value":"1367-4811","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2020,11,1]]},"published":{"date-parts":[[2020,7,23]]}}}