{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,3]],"date-time":"2026-09-03T08:43:13Z","timestamp":1788424993980,"version":"build-2803163510"},"reference-count":32,"publisher":"Oxford University Press (OUP)","issue":"2","license":[{"start":{"date-parts":[[2021,10,2]],"date-time":"2021-10-02T00:00:00Z","timestamp":1633132800000},"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\/501100003970","name":"Isfahan University of Medical Sciences","doi-asserted-by":"publisher","award":["198111"],"award-info":[{"award-number":["198111"]}],"id":[{"id":"10.13039\/501100003970","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003970","name":"Isfahan University of Medical Sciences","doi-asserted-by":"publisher","award":["298219"],"award-info":[{"award-number":["298219"]}],"id":[{"id":"10.13039\/501100003970","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003970","name":"Isfahan University of Medical Sciences","doi-asserted-by":"publisher","award":["297174"],"award-info":[{"award-number":["297174"]}],"id":[{"id":"10.13039\/501100003970","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Spanish Ministry of Economy and Competitiveness","award":["CTQ2017-87974-R"],"award-info":[{"award-number":["CTQ2017-87974-R"]}]},{"name":"Fundaci\u00f3n S\u00e9neca del Centro de Coordinaci\u00f3n de la Investigaci\u00f3n de la Regi\u00f3n de Murcia under Project","award":["20988\/PI\/18.o Powered@NLHPC"],"award-info":[{"award-number":["20988\/PI\/18.o Powered@NLHPC"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,1,3]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>The aim of quantitative structure\u2013activity prediction (QSAR) studies is to identify novel drug-like molecules that can be suggested as lead compounds by means of two approaches, which are discussed in this article. First, to identify appropriate molecular descriptors by focusing on one feature-selection algorithms; and second to predict the biological activities of designed compounds. Recent studies have shown increased interest in the prediction of a huge number of molecules, known as Big Data, using deep learning models. However, despite all these efforts to solve critical challenges in QSAR models, such as over-fitting, massive processing procedures, is major shortcomings of deep learning models. Hence, finding the most effective molecular descriptors in the shortest possible time is an ongoing task. One of the successful methods to speed up the extraction of the best features from big datasets is the use of least absolute shrinkage and selection operator (LASSO). This algorithm is a regression model that selects a subset of molecular descriptors with the aim of enhancing prediction accuracy and interpretability because of removing inappropriate and irrelevant features.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>To implement and test our proposed model, a random forest was built to predict the molecular activities of Kaggle competition compounds. Finally, the prediction results and computation time of the suggested model were compared with the other well-known algorithms, i.e. Boruta-random forest, deep random forest and deep belief network model. The results revealed that improving output correlation through LASSO-random forest leads to appreciably reduced implementation time and model complexity, while maintaining accuracy of the predictions.<\/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\/btab659","type":"journal-article","created":{"date-parts":[[2021,9,30]],"date-time":"2021-09-30T20:54:33Z","timestamp":1633035273000},"page":"469-475","source":"Crossref","is-referenced-by-count":64,"title":["Accelerating Big Data Analysis through LASSO-Random Forest Algorithm in QSAR Studies"],"prefix":"10.1093","volume":"38","author":[{"given":"Fahimeh","family":"Motamedi","sequence":"first","affiliation":[{"name":"Department of Bioinformatics and Systems Biology, School of Advanced Technologies in Medicine, Isfahan University of Medical Sciences , Isfahan 8174673461, Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Horacio","family":"P\u00e9rez-S\u00e1nchez","sequence":"additional","affiliation":[{"name":"Structural Bioinformatics and High Performance Computing Reseach Group (BIO-HPC), Computer Engineering Department, UCAM Universidad Cat\u00f3lica de Murcia , Murcia E30107, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alireza","family":"Mehridehnavi","sequence":"additional","affiliation":[{"name":"Department of Bioinformatics and Systems Biology, School of Advanced Technologies in Medicine, Isfahan University of Medical Sciences , Isfahan 8174673461, Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Afshin","family":"Fassihi","sequence":"additional","affiliation":[{"name":"Department of Medicinal Chemistry, School of Pharmacology and Pharmaceutical Sciences, Isfahan University of Medical Sciences , Isfahan 8174673461, Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9333-4699","authenticated-orcid":false,"given":"Fahimeh","family":"Ghasemi","sequence":"additional","affiliation":[{"name":"Department of Bioinformatics and Systems Biology, School of Advanced Technologies in Medicine, Isfahan University of Medical Sciences , Isfahan 8174673461, Iran"},{"name":"Biosensor Research Centre, School of Advanced Technologies in Medicine, Isfahan University of Medical Sciences , Isfahan 8174673461, Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2021,10,2]]},"reference":[{"key":"2023020108450311800_btab659-B1","doi-asserted-by":"crossref","first-page":"547","DOI":"10.1002\/cem.2741","article-title":"High-dimensional QSAR prediction of anticancer potency of imidazo[4,5-b]pyridine derivatives using adjusted adaptive LASSO","volume":"29","author":"Algamal","year":"2015","journal-title":"J. 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