{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,30]],"date-time":"2025-10-30T22:41:26Z","timestamp":1761864086448,"version":"3.37.3"},"reference-count":38,"publisher":"Oxford University Press (OUP)","issue":"11","license":[{"start":{"date-parts":[[2020,2,24]],"date-time":"2020-02-24T00:00:00Z","timestamp":1582502400000},"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":"Agencia Nacional de Promoci\u00f3n Cient\u00edfica y Tecnol\u00f3gica","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100003074","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003074","name":"ANPCyT","doi-asserted-by":"publisher","award":["PICT 2014 #2627","2018 #3384"],"award-info":[{"award-number":["PICT 2014 #2627","2018 #3384"]}],"id":[{"id":"10.13039\/501100003074","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100005746","name":"Universidad Nacional del Litoral","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100005746","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100008114","name":"UNL","doi-asserted-by":"publisher","award":["CAI+D 2016 #082"],"award-info":[{"award-number":["CAI+D 2016 #082"]}],"id":[{"id":"10.13039\/100008114","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020,6,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>In precision medicine, next-generation sequencing and novel preclinical reports have led to an increasingly large amount of results, published in the scientific literature. However, identifying novel treatments or predicting a drug response in, for example, cancer patients, from the huge amount of papers available remains a laborious and challenging work. This task can be considered a text mining problem that requires reading a lot of academic documents for identifying a small set of papers describing specific relations between key terms. Due to the infeasibility of the manual curation of these relations, computational methods that can automatically identify them from the available literature are urgently needed.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>We present DL4papers, a new method based on deep learning that is capable of analyzing and interpreting papers in order to automatically extract relevant relations between specific keywords. DL4papers receives as input a query with the desired keywords, and it returns a ranked list of papers that contain meaningful associations between the keywords. The comparison against related methods showed that our proposal outperformed them in a cancer corpus. The reliability of the DL4papers output list was also measured, revealing that 100% of the first two documents retrieved for a particular search have relevant relations, in average. This shows that our model can guarantee that in the top-2 papers of the ranked list, the relation can be effectively found. Furthermore, the model is capable of highlighting, within each document, the specific fragments that have the associations of the input keywords. This can be very useful in order to pay attention only to the highlighted text, instead of reading the full paper. We believe that our proposal could be used as an accurate tool for rapidly identifying relationships between genes and their mutations, drug responses and treatments in the context of a certain disease. This new approach can certainly be a very useful and valuable resource for the advancement of the precision medicine field.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>A web-demo is available at: http:\/\/sinc.unl.edu.ar\/web-demo\/dl4papers\/. Full source code and data are available at: https:\/\/sourceforge.net\/projects\/sourcesinc\/files\/dl4papers\/.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Contact<\/jats:title>\n                  <jats:p>lbugnon@sinc.unl.edu.ar<\/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\/btaa111","type":"journal-article","created":{"date-parts":[[2020,2,15]],"date-time":"2020-02-15T12:13:09Z","timestamp":1581768789000},"page":"3499-3506","source":"Crossref","is-referenced-by-count":4,"title":["DL4papers: a deep learning approach for the automatic interpretation of scientific articles"],"prefix":"10.1093","volume":"36","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5702-946X","authenticated-orcid":false,"given":"L A","family":"Bugnon","sequence":"first","affiliation":[{"name":"Research Institute for Signals , Systems and Computational Intelligence, sinc( i ), FICH\/UNL-CONICET, Ciudad Universitaria, Santa Fe 3000, Argentina"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4157-2350","authenticated-orcid":false,"given":"C","family":"Yones","sequence":"additional","affiliation":[{"name":"Research Institute for Signals , Systems and Computational Intelligence, sinc( i ), FICH\/UNL-CONICET, Ciudad Universitaria, Santa Fe 3000, Argentina"}]},{"given":"J","family":"Raad","sequence":"additional","affiliation":[{"name":"Research Institute for Signals , Systems and Computational Intelligence, sinc( i ), FICH\/UNL-CONICET, Ciudad Universitaria, Santa Fe 3000, Argentina"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5145-5943","authenticated-orcid":false,"given":"M","family":"Gerard","sequence":"additional","affiliation":[{"name":"Research Institute for Signals , Systems and Computational Intelligence, sinc( i ), FICH\/UNL-CONICET, Ciudad Universitaria, Santa Fe 3000, Argentina"}]},{"given":"M","family":"Rubiolo","sequence":"additional","affiliation":[{"name":"Research Institute for Signals , Systems and Computational Intelligence, sinc( i ), FICH\/UNL-CONICET, Ciudad Universitaria, Santa Fe 3000, Argentina"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0130-6226","authenticated-orcid":false,"given":"G","family":"Merino","sequence":"additional","affiliation":[{"name":"Research Institute for Signals , Systems and Computational Intelligence, sinc( i ), FICH\/UNL-CONICET, Ciudad Universitaria, Santa Fe 3000, Argentina"},{"name":"Bioengineering and Bioinformatics Research and Development Institute , IBB, FIUNER-CONICET, Ruta Prov 11, Km 10.5, Oro Verde 3100, Argentina"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3035-4403","authenticated-orcid":false,"given":"M","family":"Pividori","sequence":"additional","affiliation":[{"name":"Research Institute for Signals , Systems and Computational Intelligence, sinc( i ), FICH\/UNL-CONICET, Ciudad Universitaria, Santa Fe 3000, Argentina"},{"name":"Department of Systems Pharmacology and Translational Therapeutics , University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0331-6989","authenticated-orcid":false,"given":"L","family":"Di Persia","sequence":"additional","affiliation":[{"name":"Research Institute for Signals , Systems and Computational Intelligence, sinc( i ), FICH\/UNL-CONICET, Ciudad Universitaria, Santa Fe 3000, Argentina"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2182-4351","authenticated-orcid":false,"given":"D H","family":"Milone","sequence":"additional","affiliation":[{"name":"Research Institute for Signals , Systems and Computational Intelligence, sinc( i ), FICH\/UNL-CONICET, Ciudad Universitaria, Santa Fe 3000, Argentina"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4459-4560","authenticated-orcid":false,"given":"G","family":"Stegmayer","sequence":"additional","affiliation":[{"name":"Research Institute for Signals , Systems and Computational Intelligence, sinc( i ), FICH\/UNL-CONICET, Ciudad Universitaria, Santa Fe 3000, Argentina"}]}],"member":"286","published-online":{"date-parts":[[2020,2,24]]},"reference":[{"key":"2023062312015479700_btaa111-B1","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1162\/tacl_a_00051","article-title":"Enriching word vectors with subword information","volume":"5","author":"Bojanowski","year":"2017","journal-title":"Trans. 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