{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,8]],"date-time":"2026-04-08T01:03:38Z","timestamp":1775610218156,"version":"3.50.1"},"reference-count":61,"publisher":"Oxford University Press (OUP)","issue":"4","license":[{"start":{"date-parts":[[2024,3,20]],"date-time":"2024-03-20T00:00:00Z","timestamp":1710892800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62302357"],"award-info":[{"award-number":["62302357"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100017596","name":"Natural Science Basic Research Program of Shaanxi Province","doi-asserted-by":"publisher","award":["S2023-JC-QN-0727"],"award-info":[{"award-number":["S2023-JC-QN-0727"]}],"id":[{"id":"10.13039\/501100017596","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["ZYTS24088"],"award-info":[{"award-number":["ZYTS24088"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,3,29]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>The diverse structures and functions inherent in RNAs present a wealth of potential drug targets. Some small molecules are anticipated to serve as leading compounds, providing guidance for the development of novel RNA-targeted therapeutics. Consequently, the determination of RNA\u2013small molecule binding affinity is a critical undertaking in the landscape of RNA-targeted drug discovery and development. Nevertheless, to date, only one computational method for RNA\u2013small molecule binding affinity prediction has been proposed. The prediction of RNA\u2013small molecule binding affinity remains a significant challenge. The development of a computational model is deemed essential to effectively extract relevant features and predict RNA\u2013small molecule binding affinity accurately.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>In this study, we introduced RLaffinity, a novel deep learning model designed for the prediction of RNA\u2013small molecule binding affinity based on 3D structures. RLaffinity integrated information from RNA pockets and small molecules, utilizing a 3D convolutional neural network (3D-CNN) coupled with a contrastive learning-based self-supervised pre-training model. To the best of our knowledge, RLaffinity was the first deep learning based method for the prediction of RNA\u2013small molecule binding affinity. Our experimental results exhibited RLaffinity\u2019s superior performance compared to baseline methods, revealed by all metrics. The efficacy of RLaffinity underscores the capability of 3D-CNN to accurately extract both global pocket information and local neighbor nucleotide information within RNAs. Notably, the integration of a self-supervised pre-training model significantly enhanced predictive performance. Ultimately, RLaffinity was also proved as a potential tool for RNA-targeted drugs virtual screening.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>https:\/\/github.com\/SaisaiSun\/RLaffinity<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btae155","type":"journal-article","created":{"date-parts":[[2024,3,18]],"date-time":"2024-03-18T14:40:21Z","timestamp":1710772821000},"source":"Crossref","is-referenced-by-count":28,"title":["Contrastive pre-training and 3D convolution neural network for RNA and small molecule binding affinity prediction"],"prefix":"10.1093","volume":"40","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-6090-3030","authenticated-orcid":false,"given":"Saisai","family":"Sun","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology, Xidian University , No.266 Xinglong Section of Xi Feng Road , Xi\u2019an, Shaanxi, 710126, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6396-0787","authenticated-orcid":false,"given":"Lin","family":"Gao","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Xidian University , No.266 Xinglong Section of Xi Feng Road , Xi\u2019an, Shaanxi, 710126, China"}]}],"member":"286","published-online":{"date-parts":[[2024,3,20]]},"reference":[{"key":"2024041105310069300_btae155-B1","doi-asserted-by":"crossref","first-page":"93","DOI":"10.4155\/fmc.09.149","article-title":"Strategies for the design of RNA-binding small molecules","volume":"2","author":"Aboul-Ela","year":"2010","journal-title":"Future Med Chem"},{"key":"2024041105310069300_btae155-B2","doi-asserted-by":"crossref","first-page":"6659083","DOI":"10.1155\/2021\/6659083","article-title":"Deep residual network in network","volume":"2021","author":"Alaeddine","year":"2021","journal-title":"Comput Intell Neurosci"},{"key":"2024041105310069300_btae155-B3","doi-asserted-by":"crossref","first-page":"1169","DOI":"10.1093\/bioinformatics\/btq112","article-title":"A machine learning approach to predicting protein-ligand binding affinity with applications to molecular docking","volume":"26","author":"Ballester","year":"2010","journal-title":"Bioinformatics"},{"key":"2024041105310069300_btae155-B4","doi-asserted-by":"crossref","first-page":"509","DOI":"10.1145\/361002.361007","article-title":"Multidimensional binary search trees used for associative searching","volume":"18","author":"Bentley","year":"1975","journal-title":"Commun ACM"},{"key":"2024041105310069300_btae155-B5","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1107\/S0108767307035623","article-title":"The protein data bank: a historical perspective","volume":"64","author":"Berman","year":"2008","journal-title":"Acta Crystallogr A"},{"key":"2024041105310069300_btae155-B6","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1016\/j.neuron.2015.06.012","article-title":"RNA structures as mediators of neurological diseases and as drug targets","volume":"87","author":"Bernat","year":"2015","journal-title":"Neuron"},{"key":"2024041105310069300_btae155-B7","doi-asserted-by":"crossref","first-page":"2289","DOI":"10.2741\/3854","article-title":"Protein-ligand docking","volume":"16","author":"Bottegoni","year":"2011","journal-title":"Front Biosci (Landmark Ed)"},{"key":"2024041105310069300_btae155-B8","doi-asserted-by":"crossref","first-page":"8880","DOI":"10.1021\/acs.jmedchem.9b01927","article-title":"How we think about targeting RNA with small molecules","volume":"63","author":"Costales","year":"2020","journal-title":"J Med Chem"},{"key":"2024041105310069300_btae155-B9","doi-asserted-by":"crossref","first-page":"e29","DOI":"10.1093\/nar\/gnj031","article-title":"Nucleic acid visualization with UCSF chimera","volume":"34","author":"Couch","year":"2006","journal-title":"Nucleic Acids Res"},{"key":"2024041105310069300_btae155-B10","doi-asserted-by":"crossref","first-page":"103406","DOI":"10.1016\/j.cviu.2022.103406","article-title":"TCLR: Temporal contrastive learning for video representation","volume":"219","author":"Dave","year":"2022","journal-title":"Computer Vision and Image Understanding"},{"key":"2024041105310069300_btae155-B11","first-page":"2705","article-title":"Similarity contrastive estimation for self-supervised soft contrastive learning","author":"Denize","year":"2023","journal-title":"IEEE Wint Conf Appl"},{"key":"2024041105310069300_btae155-B12","doi-asserted-by":"crossref","DOI":"10.1002\/wrna.1381","article-title":"Modulating splicing with small molecular inhibitors of the spliceosome","volume":"8","author":"Effenberger","year":"2017","journal-title":"Wiley Interdiscip Rev RNA"},{"key":"2024041105310069300_btae155-B13","doi-asserted-by":"crossref","first-page":"431","DOI":"10.1080\/15476286.2023.2231708","article-title":"Characterizing RNA-binding ligands on structures, chemical information, binding affinity and drug-likeness","volume":"20","author":"Fan","year":"2023","journal-title":"RNA Biol"},{"key":"2024041105310069300_btae155-B14","doi-asserted-by":"crossref","first-page":"6698","DOI":"10.1021\/acs.jcim.0c00974","article-title":"ITScore-NL: an iterative knowledge-based scoring function for nucleic acid-ligand interactions","volume":"60","author":"Feng","year":"2020","journal-title":"J Chem Inf Model"},{"key":"2024041105310069300_btae155-B15","doi-asserted-by":"crossref","first-page":"4771","DOI":"10.1021\/acs.jcim.1c00341","article-title":"NLDock: a fast nucleic acid-ligand docking algorithm for modeling RNA\/DNA-ligand complexes","volume":"61","author":"Feng","year":"2021","journal-title":"J Chem Inf Model"},{"key":"2024041105310069300_btae155-B16","doi-asserted-by":"crossref","first-page":"487","DOI":"10.1186\/s12951-022-01696-z","article-title":"Uterine macrophages as treatment targets for therapy of premature rupture of membranes by modified ADSC-EVs through a circRNA\/miRNA\/NF-kappaB pathway","volume":"20","author":"Gao","year":"2022","journal-title":"J Nanobiotechnology"},{"key":"2024041105310069300_btae155-B17","doi-asserted-by":"crossref","first-page":"2644","DOI":"10.1002\/1521-3773(20020802)41:15<2644::AID-ANIE2644>3.0.CO;2-O","article-title":"Approaches to the description and prediction of the binding affinity of small-molecule ligands to macromolecular receptors","volume":"41","author":"Gohlke","year":"2002","journal-title":"Angew Chem Int Ed"},{"key":"2024041105310069300_btae155-B18","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1002\/pro.3934","article-title":"The AutoDock suite at 30","volume":"30","author":"Goodsell","year":"2021","journal-title":"Protein Sci"},{"key":"2024041105310069300_btae155-B19","doi-asserted-by":"crossref","first-page":"1257","DOI":"10.1021\/ci8000327","article-title":"Docking to RNA via root-mean-square-deviation-driven energy minimization with flexible ligands and flexible targets","volume":"48","author":"Guilbert","year":"2008","journal-title":"J Chem Inf Model"},{"key":"2024041105310069300_btae155-B20","doi-asserted-by":"crossref","first-page":"3036","DOI":"10.1093\/bioinformatics\/btx350","article-title":"DeepSite: protein-binding site predictor using 3D-convolutional neural networks","volume":"33","author":"Jim\u00e9nez","year":"2017","journal-title":"Bioinformatics"},{"key":"2024041105310069300_btae155-B21","doi-asserted-by":"crossref","first-page":"287","DOI":"10.1021\/acs.jcim.7b00650","article-title":"K(DEEP): protein-ligand absolute binding affinity prediction via 3D-convolutional neural networks","volume":"58","author":"Jim\u00e9nez","year":"2018","journal-title":"J Chem Inf Model"},{"key":"2024041105310069300_btae155-B22","doi-asserted-by":"crossref","first-page":"1583","DOI":"10.1021\/acs.jcim.0c01306","article-title":"Improved protein-ligand binding affinity prediction with structure-based deep fusion inference","volume":"61","author":"Jones","year":"2021","journal-title":"J Chem Inf Model"},{"key":"2024041105310069300_btae155-B23","doi-asserted-by":"crossref","DOI":"10.1093\/bib\/bbae002","article-title":"Reliable method for predicting the binding affinity of RNA-small molecule interactions using machine learning","volume":"25","author":"Krishnan","year":"2024","journal-title":"Brief Bioinform"},{"key":"2024041105310069300_btae155-B24","doi-asserted-by":"crossref","first-page":"4378","DOI":"10.1021\/acs.molpharmaceut.7b01134","article-title":"3D molecular representations based on the wave transform for convolutional neural networks","volume":"15","author":"Kuzminykh","year":"2018","journal-title":"Mol Pharm"},{"key":"2024041105310069300_btae155-B25","doi-asserted-by":"crossref","first-page":"1219","DOI":"10.1261\/rna.1563609","article-title":"DOCK 6: combining techniques to model RNA-small molecule complexes","volume":"15","author":"Lang","year":"2009","journal-title":"RNA"},{"key":"2024041105310069300_btae155-B26","doi-asserted-by":"crossref","first-page":"672","DOI":"10.1038\/nm.4097","article-title":"Modulation of splicing catalysis for therapeutic targeting of leukemia with mutations in genes encoding spliceosomal proteins","volume":"22","author":"Lee","year":"2016","journal-title":"Nat Med"},{"key":"2024041105310069300_btae155-B27","doi-asserted-by":"crossref","first-page":"D132","DOI":"10.1093\/nar\/gkt976","article-title":"SMMRNA: a database of small molecule modulators of RNA","volume":"42","author":"Mehta","year":"2014","journal-title":"Nucleic Acids Res"},{"key":"2024041105310069300_btae155-B28","doi-asserted-by":"crossref","first-page":"13498","DOI":"10.1002\/anie.201707641","article-title":"Discovery of key physicochemical, structural, and spatial properties of RNA-targeted bioactive ligands","volume":"56","author":"Morgan","year":"2017","journal-title":"Angew Chem Int Ed Engl"},{"key":"2024041105310069300_btae155-B29","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1023\/B:JCAM.0000035199.48747.1e","article-title":"Validation of an empirical RNA-ligand scoring function for fast flexible docking using Ribodock","volume":"18","author":"Morley","year":"2004","journal-title":"J Comput Aided Mol Des"},{"key":"2024041105310069300_btae155-B30","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1186\/1752-153X-2-5","article-title":"Pybel: a Python wrapper for the OpenBabel cheminformatics toolkit","volume":"2","author":"O\u2019Boyle","year":"2008","journal-title":"Chem Cent J"},{"key":"2024041105310069300_btae155-B31","doi-asserted-by":"crossref","first-page":"1208","DOI":"10.1021\/acs.jctc.0c00931","article-title":"Assessment of AMBER force fields for simulations of ssDNA","volume":"17","author":"Oweida","year":"2021","journal-title":"J Chem Theory Comput"},{"key":"2024041105310069300_btae155-B32","doi-asserted-by":"crossref","first-page":"4185","DOI":"10.1093\/bioinformatics\/btac483","article-title":"HARIBOSS: a curated database of RNA-small molecules structures to aid rational drug design","volume":"38","author":"Panei","year":"2022","journal-title":"Bioinformatics"},{"key":"2024041105310069300_btae155-B33","doi-asserted-by":"crossref","first-page":"1868","DOI":"10.1021\/ci700134p","article-title":"DrugScoreRNA\u2014knowledge-based scoring function to predict RNA-ligand interactions","volume":"47","author":"Pfeffer","year":"2007","journal-title":"J Chem Inf Model"},{"key":"2024041105310069300_btae155-B34","doi-asserted-by":"crossref","first-page":"198","DOI":"10.1093\/bioinformatics\/btr636","article-title":"MetalionRNA: computational predictor of metal-binding sites in RNA structures","volume":"28","author":"Philips","year":"2012","journal-title":"Bioinformatics"},{"key":"2024041105310069300_btae155-B35","doi-asserted-by":"crossref","first-page":"1605","DOI":"10.1261\/rna.039834.113","article-title":"LigandRNA: computational predictor of RNA-ligand interactions","volume":"19","author":"Philips","year":"2013","journal-title":"RNA"},{"key":"2024041105310069300_btae155-B36","doi-asserted-by":"crossref","first-page":"942","DOI":"10.1021\/acs.jcim.6b00740","article-title":"Protein-ligand scoring with convolutional neural networks","volume":"57","author":"Ragoza","year":"2017","journal-title":"J Chem Inf Model"},{"key":"2024041105310069300_btae155-B37","doi-asserted-by":"crossref","first-page":"e1003571","DOI":"10.1371\/journal.pcbi.1003571","article-title":"rDock: a fast, versatile and open source program for docking ligands to proteins and nucleic acids","volume":"10","author":"Ruiz-Carmona","year":"2014","journal-title":"PLoS Comput Biol"},{"key":"2024041105310069300_btae155-B38","doi-asserted-by":"crossref","first-page":"233","DOI":"10.1038\/nature07642","article-title":"Coenzyme recognition and gene regulation by a flavin mononucleotide riboswitch","volume":"458","author":"Serganov","year":"2009","journal-title":"Nature"},{"key":"2024041105310069300_btae155-B39","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1002\/prot.21082","article-title":"Protein-ligand docking: current status and future challenges","volume":"65","author":"Sousa","year":"2006","journal-title":"Proteins"},{"key":"2024041105310069300_btae155-B40","doi-asserted-by":"crossref","first-page":"553","DOI":"10.1038\/nchembio.596","article-title":"Discovery of selective bioactive small molecules by targeting an RNA dynamic ensemble","volume":"7","author":"Stelzer","year":"2011","journal-title":"Nat Chem Biol"},{"key":"2024041105310069300_btae155-B41","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1093\/bioinformatics\/btaa1092","article-title":"Recognition of small molecule-RNA binding sites using RNA sequence and structure","volume":"37","author":"Su","year":"2021","journal-title":"Bioinformatics"},{"key":"2024041105310069300_btae155-B4907221","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1261\/rna.078889.121","article-title":"RNALigands: A database and web server for rna\u2013ligand interactions","volume":"28","author":"Sun","year":"2022","journal-title":"RNA"},{"key":"2024041105310069300_btae155-B42","doi-asserted-by":"crossref","first-page":"e1009783","DOI":"10.1371\/journal.pcbi.1009783","article-title":"fingeRNAt-a novel tool for high-throughput analysis of nucleic acid-ligand interactions","volume":"18","author":"Szulc","year":"2022","journal-title":"PLoS Comput Biol"},{"key":"2024041105310069300_btae155-B43","doi-asserted-by":"crossref","first-page":"270","DOI":"10.1038\/s41419-021-03565-3","article-title":"CircRNA circ_0124554 blocked the ubiquitination of AKT promoting the skip lymphovascular invasion on hepatic metastasis in colorectal cancer","volume":"12","author":"Tang","year":"2021","journal-title":"Cell Death Dis"},{"key":"2024041105310069300_btae155-B44","doi-asserted-by":"crossref","first-page":"1171","DOI":"10.1021\/cr0681546","article-title":"Targeting RNA with small molecules","volume":"108","author":"Thomas","year":"2008","journal-title":"Chem Rev"},{"key":"2024041105310069300_btae155-B45","doi-asserted-by":"crossref","first-page":"1241","DOI":"10.1016\/j.chembiol.2020.07.021","article-title":"Parallel discovery strategies provide a basis for riboswitch ligand design","volume":"27","author":"Tran","year":"2020","journal-title":"Cell Chem Biol"},{"key":"2024041105310069300_btae155-B46","doi-asserted-by":"crossref","first-page":"455","DOI":"10.1002\/jcc.21334","article-title":"AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading","volume":"31","author":"Trott","year":"2010","journal-title":"J Comput Chem"},{"key":"2024041105310069300_btae155-B47","doi-asserted-by":"crossref","first-page":"2977","DOI":"10.1021\/jm030580l","article-title":"The PDBbind database: collection of binding affinities for protein-ligand complexes with known three-dimensional structures","volume":"47","author":"Wang","year":"2004","journal-title":"J Med Chem"},{"key":"2024041105310069300_btae155-B48","doi-asserted-by":"crossref","first-page":"4111","DOI":"10.1021\/jm048957q","article-title":"The PDBbind database: methodologies and updates","volume":"48","author":"Wang","year":"2005","journal-title":"J Med Chem"},{"key":"2024041105310069300_btae155-B49","doi-asserted-by":"crossref","first-page":"3131","DOI":"10.1093\/bioinformatics\/bty345","article-title":"RBind: computational network method to predict RNA binding sites","volume":"34","author":"Wang","year":"2018","journal-title":"Bioinformatics"},{"key":"2024041105310069300_btae155-B50","first-page":"3023","article-title":"Dense contrastive learning for self-supervised visual pre-training","author":"Wang","year":"2021","journal-title":"Proc Cvpr Ieee"},{"key":"2024041105310069300_btae155-B51","article-title":"RLBind: a deep learning method to predict RNA-ligand binding sites","volume":"24","author":"Wang","year":"2023","journal-title":"Brief Bioinform"},{"key":"2024041105310069300_btae155-B52","doi-asserted-by":"crossref","first-page":"D520","DOI":"10.1093\/nar\/gky949","article-title":"Protein Data Bank: the single global archive for 3D macromolecular structure data","volume":"47","author":"wwPDB consortium","year":"2019","journal-title":"Nucleic Acids Res"},{"key":"2024041105310069300_btae155-B53","doi-asserted-by":"crossref","first-page":"e1009986","DOI":"10.1371\/journal.pcbi.1009986","article-title":"Fast protein structure comparison through effective representation learning with contrastive graph neural networks","volume":"18","author":"Xia","year":"2022","journal-title":"PLoS Comput Biol"},{"key":"2024041105310069300_btae155-B54","first-page":"8372","author":"Xie","year":"2021"},{"key":"2024041105310069300_btae155-B55","doi-asserted-by":"crossref","first-page":"755","DOI":"10.1016\/j.ejmech.2017.11.079","article-title":"Purine analogs targeting the guanine riboswitch as potential antibiotics against Clostridioides difficile","volume":"143","author":"Yan","year":"2018","journal-title":"Eur J Med Chem"},{"key":"2024041105310069300_btae155-B56","doi-asserted-by":"crossref","first-page":"e110","DOI":"10.1093\/nar\/gkx255","article-title":"SPA-LN: a scoring function of ligand-nucleic acid interactions via optimizing both specificity and affinity","volume":"45","author":"Yan","year":"2017","journal-title":"Nucleic Acids Res"},{"key":"2024041105310069300_btae155-B57","doi-asserted-by":"crossref","first-page":"9179","DOI":"10.1038\/srep09179","article-title":"Rsite: a computational method to identify the functional sites of noncoding RNAs","volume":"5","author":"Zeng","year":"2015","journal-title":"Sci Rep"},{"key":"2024041105310069300_btae155-B58","first-page":"10603","author":"Zhao","year":"2021"},{"key":"2024041105310069300_btae155-B59","doi-asserted-by":"crossref","first-page":"428","DOI":"10.1186\/s12859-021-04349-4","article-title":"RPocket: an intuitive database of RNA pocket topology information with RNA-ligand data resources","volume":"22","author":"Zhou","year":"2021","journal-title":"BMC Bioinformatics"},{"key":"2024041105310069300_btae155-B60","doi-asserted-by":"crossref","first-page":"2736","DOI":"10.3390\/ijms23052736","article-title":"Current advances in RNA therapeutics for human diseases","volume":"23","author":"Zogg","year":"2022","journal-title":"Int J Mol Sci"}],"container-title":["Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bioinformatics\/advance-article-pdf\/doi\/10.1093\/bioinformatics\/btae155\/57037968\/btae155.pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/40\/4\/btae155\/57212204\/btae155.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/40\/4\/btae155\/57212204\/btae155.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,4,11]],"date-time":"2024-04-11T05:31:41Z","timestamp":1712813501000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article\/doi\/10.1093\/bioinformatics\/btae155\/7632736"}},"subtitle":[],"editor":[{"given":"Arne","family":"Elofsson","sequence":"additional","affiliation":[]}],"short-title":[],"issued":{"date-parts":[[2024,3,20]]},"references-count":61,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2024,3,29]]}},"URL":"https:\/\/doi.org\/10.1093\/bioinformatics\/btae155","relation":{},"ISSN":["1367-4811"],"issn-type":[{"value":"1367-4811","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2024,4,1]]},"published":{"date-parts":[[2024,3,20]]},"article-number":"btae155"}}