{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,5]],"date-time":"2026-06-05T05:15:38Z","timestamp":1780636538993,"version":"3.54.1"},"reference-count":32,"publisher":"Oxford University Press (OUP)","issue":"3","license":[{"start":{"date-parts":[[2021,10,22]],"date-time":"2021-10-22T00:00:00Z","timestamp":1634860800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,1,12]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>MicroRNAs (miRNAs) play pivotal roles in gene expression regulation by binding to target sites of messenger RNAs (mRNAs). While identifying functional targets of miRNAs is of utmost importance, their prediction remains a great challenge. Previous computational algorithms have major limitations. They use conservative candidate target site (CTS) selection criteria mainly focusing on canonical site types, rely on laborious and time-consuming manual feature extraction, and do not fully capitalize on the information underlying miRNA\u2013CTS interactions.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>In this article, we introduce TargetNet, a novel deep learning-based algorithm for functional miRNA target prediction. To address the limitations of previous approaches, TargetNet has three key components: (i) relaxed CTS selection criteria accommodating irregularities in the seed region, (ii) a novel miRNA\u2013CTS sequence encoding scheme incorporating extended seed region alignments and (iii) a deep residual network-based prediction model. The proposed model was trained with miRNA\u2013CTS pair datasets and evaluated with miRNA\u2013mRNA pair datasets. TargetNet advances the previous state-of-the-art algorithms used in functional miRNA target classification. Furthermore, it demonstrates great potential for distinguishing high-functional miRNA targets.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>The codes and pre-trained models are available at https:\/\/github.com\/mswzeus\/TargetNet.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btab733","type":"journal-article","created":{"date-parts":[[2021,10,19]],"date-time":"2021-10-19T13:49:41Z","timestamp":1634651381000},"page":"671-677","source":"Crossref","is-referenced-by-count":65,"title":["TargetNet: functional microRNA target prediction with deep neural networks"],"prefix":"10.1093","volume":"38","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3278-0211","authenticated-orcid":false,"given":"Seonwoo","family":"Min","sequence":"first","affiliation":[{"name":"Department of Electrical and Computer Engineering, Seoul National University , Seoul 08826, South Korea"},{"name":"LG AI Research , Seoul 07796, South Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Byunghan","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Electronic and IT Media Engineering, Seoul National University of Science and Technology , Seoul 01811, South Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2367-197X","authenticated-orcid":false,"given":"Sungroh","family":"Yoon","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, Seoul National University , Seoul 08826, South Korea"},{"name":"Interdisciplinary Program in Artificial Intelligence and Interdisciplinary Program in Bioinformatics, Seoul National University , Seoul 08826, South Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2021,10,22]]},"reference":[{"key":"2023020108475214100_btab733-B1","doi-asserted-by":"crossref","first-page":"e05005","DOI":"10.7554\/eLife.05005","article-title":"Predicting effective microRNA target sites in mammalian mRNAs","volume":"4","author":"Agarwal","year":"2015","journal-title":"eLife"},{"key":"2023020108475214100_btab733-B2","doi-asserted-by":"crossref","first-page":"215","DOI":"10.1016\/j.cell.2009.01.002","article-title":"MicroRNAs: target recognition and regulatory functions","volume":"136","author":"Bartel","year":"2009","journal-title":"Cell"},{"key":"2023020108475214100_btab733-B3","doi-asserted-by":"crossref","first-page":"R90","DOI":"10.1186\/gb-2010-11-8-r90","article-title":"Comprehensive modeling of microRNA targets predicts functional non-conserved and non-canonical sites","volume":"11","author":"Betel","year":"2010","journal-title":"Genome Biol"},{"key":"2023020108475214100_btab733-B4","doi-asserted-by":"crossref","first-page":"320","DOI":"10.1016\/j.molcel.2016.09.004","article-title":"Pairing beyond the seed supports microRNA targeting specificity","volume":"64","author":"Broughton","year":"2016","journal-title":"Mol. 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