{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,8]],"date-time":"2026-04-08T16:28:47Z","timestamp":1775665727428,"version":"3.50.1"},"reference-count":41,"publisher":"Oxford University Press (OUP)","issue":"5","license":[{"start":{"date-parts":[[2021,12,7]],"date-time":"2021-12-07T00:00:00Z","timestamp":1638835200000},"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\/501100003074","name":"ANPCyT","doi-asserted-by":"publisher","award":["PICT 2018 #3384 and PICT 2018 #2905"],"award-info":[{"award-number":["PICT 2018 #3384 and PICT 2018 #2905"]}],"id":[{"id":"10.13039\/501100003074","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100008114","name":"UNL","doi-asserted-by":"publisher","award":["CAI+D 2016 #082 and CAID 2020 #115"],"award-info":[{"award-number":["CAI+D 2016 #082 and CAID 2020 #115"]}],"id":[{"id":"10.13039\/100008114","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,2,7]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>MicroRNAs (miRNAs) are small RNA sequences with key roles in the regulation of gene expression at post-transcriptional level in different species. Accurate prediction of novel miRNAs is needed due to their importance in many biological processes and their associations with complicated diseases in humans. Many machine learning approaches were proposed in the last decade for this purpose, but requiring handcrafted features extraction to identify possible de novo miRNAs. More recently, the emergence of deep learning (DL) has allowed the automatic feature extraction, learning relevant representations by themselves. However, the state-of-art deep models require complex pre-processing of the input sequences and prediction of their secondary structure to reach an acceptable performance.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>In this work, we present miRe2e, the first full end-to-end DL model for pre-miRNA prediction. This model is based on Transformers, a neural architecture that uses attention mechanisms to infer global dependencies between inputs and outputs. It is capable of receiving the raw genome-wide data as input, without any pre-processing nor feature engineering. After a training stage with known pre-miRNAs, hairpin and non-harpin sequences, it can identify all the pre-miRNA sequences within a genome. The model has been validated through several experimental setups using the human genome, and it was compared with state-of-the-art algorithms obtaining 10 times better performance.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>Webdemo available at https:\/\/sinc.unl.edu.ar\/web-demo\/miRe2e\/ and source code available for download at https:\/\/github.com\/sinc-lab\/miRe2e.<\/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\/btab823","type":"journal-article","created":{"date-parts":[[2021,12,1]],"date-time":"2021-12-01T20:22:18Z","timestamp":1638390138000},"page":"1191-1197","source":"Crossref","is-referenced-by-count":28,"title":["miRe2e: a full end-to-end deep model based on transformers for prediction of pre-miRNAs"],"prefix":"10.1093","volume":"38","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9585-1531","authenticated-orcid":false,"given":"Jonathan","family":"Raad","sequence":"first","affiliation":[{"name":"Informatics Department, Research Institute for Signals, Systems and Computational Intelligence sinc(i) (FICH-UNL\/CONICET), Ciudad Universitaria , Santa Fe, Argentina"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5702-946X","authenticated-orcid":false,"given":"Leandro A","family":"Bugnon","sequence":"additional","affiliation":[{"name":"Informatics Department, Research Institute for Signals, Systems and Computational Intelligence sinc(i) (FICH-UNL\/CONICET), Ciudad Universitaria , Santa Fe, Argentina"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2182-4351","authenticated-orcid":false,"given":"Diego H","family":"Milone","sequence":"additional","affiliation":[{"name":"Informatics Department, Research Institute for Signals, Systems and Computational Intelligence sinc(i) (FICH-UNL\/CONICET), Ciudad Universitaria , Santa Fe, Argentina"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4459-4560","authenticated-orcid":false,"given":"Georgina","family":"Stegmayer","sequence":"additional","affiliation":[{"name":"Informatics Department, Research Institute for Signals, Systems and Computational Intelligence sinc(i) (FICH-UNL\/CONICET), Ciudad Universitaria , Santa Fe, Argentina"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2021,12,7]]},"reference":[{"key":"2023020108550330400_btab823-B1","doi-asserted-by":"crossref","first-page":"246","DOI":"10.1038\/s42256-019-0051-2","article-title":"Evaluation of deep learning in non-coding RNA classification","volume":"1","author":"Amin","year":"2019","journal-title":"Nat. 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