{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,13]],"date-time":"2026-05-13T07:57:51Z","timestamp":1778659071815,"version":"3.51.4"},"reference-count":22,"publisher":"Oxford University Press (OUP)","issue":"7","license":[{"start":{"date-parts":[[2017,11,22]],"date-time":"2017-11-22T00:00:00Z","timestamp":1511308800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/about_us\/legal\/notices"}],"funder":[{"DOI":"10.13039\/501100002923","name":"National Scientific and Technical Research Council","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100002923","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002923","name":"CONICET","doi-asserted-by":"publisher","award":["PIP 2013 117"],"award-info":[{"award-number":["PIP 2013 117"]}],"id":[{"id":"10.13039\/501100002923","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100008114","name":"UNL","doi-asserted-by":"publisher","award":["CAI+D 2011 548, 2016 076, 2016 082"],"award-info":[{"award-number":["CAI+D 2011 548, 2016 076, 2016 082"]}],"id":[{"id":"10.13039\/100008114","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100009483","name":"National Technological University","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100009483","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100009483","name":"UTN","doi-asserted-by":"publisher","award":["EIUTNFE0004442"],"award-info":[{"award-number":["EIUTNFE0004442"]}],"id":[{"id":"10.13039\/100009483","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003074","name":"ANPCyT","doi-asserted-by":"publisher","award":["PICT 2014 2627"],"award-info":[{"award-number":["PICT 2014 2627"]}],"id":[{"id":"10.13039\/501100003074","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2018,4,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>The reconstruction of gene regulatory networks (GRNs) from genes profiles has a growing interest in bioinformatics for understanding the complex regulatory mechanisms in cellular systems. GRNs explicitly represent the cause\u2013effect of regulation among a group of genes and its reconstruction is today a challenging computational problem. Several methods were proposed, but most of them require different input sources to provide an acceptable prediction. Thus, it is a great challenge to reconstruct a GRN only from temporal gene expression data.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>Extreme Learning Machine (ELM) is a new supervised neural model that has gained interest in the last years because of its higher learning rate and better performance than existing supervised models in terms of predictive power. This work proposes a novel approach for GRNs reconstruction in which ELMs are used for modeling the relationships between gene expression time series. Artificial datasets generated with the well-known benchmark tool used in DREAM competitions were used. Real datasets were used for validation of this novel proposal with well-known GRNs underlying the time series. The impact of increasing the size of GRNs was analyzed in detail for the compared methods. The results obtained confirm the superiority of the ELM approach against very recent state-of-the-art methods in the same experimental conditions.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>The web demo can be found at http:\/\/sinc.unl.edu.ar\/web-demo\/elm-grnnminer\/. The source code is available at https:\/\/sourceforge.net\/projects\/sourcesinc\/files\/elm-grnnminer.<\/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\/btx730","type":"journal-article","created":{"date-parts":[[2017,11,21]],"date-time":"2017-11-21T20:26:12Z","timestamp":1511295972000},"page":"1253-1260","source":"Crossref","is-referenced-by-count":31,"title":["Extreme learning machines for reverse engineering of gene regulatory networks from expression time series"],"prefix":"10.1093","volume":"34","author":[{"given":"M","family":"Rubiolo","sequence":"first","affiliation":[{"name":"Research Institute for Signals, Systems and Computational Intelligence, sinc(i), FICH\/UNL-CONICET, Ciudad Universitaria, Santa Fe, Argentina"},{"name":"Center of Research and Development of Information System Engineering, CIDISI, System Engineering Department, UTN-FRSF, Santa Fe, Argentina"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"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, Argentina"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"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, Argentina"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2017,11,22]]},"reference":[{"key":"2023012713004005000_btx730-B1","doi-asserted-by":"crossref","first-page":"172","DOI":"10.1016\/j.cell.2009.01.055","article-title":"A yeast synthetic network for in vivo assessment of reverse-engineering and modeling approaches","volume":"137","author":"Cantone","year":"2009","journal-title":"Cell"},{"key":"2023012713004005000_btx730-B2","doi-asserted-by":"crossref","first-page":"298","DOI":"10.1016\/j.ymeth.2014.06.005","article-title":"De novo reconstruction of gene regulatory networks from time series data, an approach based on formal methods","volume":"69","author":"Ceccarelli","year":"2014","journal-title":"Methods"},{"key":"2023012713004005000_btx730-B3","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1016\/j.compbiomed.2014.02.011","article-title":"A review on the computational approaches for gene regulatory network construction","volume":"48","author":"Chai","year":"2014","journal-title":"Comput. 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