Please use this identifier to cite or link to this item: https://hdl.handle.net/11147/11861
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dc.contributor.authorÇakı, Onuren_US
dc.contributor.authorKaraçalı, Bilgeen_US
dc.date.accessioned2021-12-14T11:55:19Z-
dc.date.available2021-12-14T11:55:19Z-
dc.date.issued2021-
dc.identifier.urihttps://hdl.handle.net/11147/11861-
dc.identifier.urihttps://doi.org/10.1002/minf.202100118-
dc.description.abstractIn-silico compound-protein interaction prediction addresses prioritization of drug candidates for experimental biochemical validation because the wet-lab experiments are time-consuming, laborious and costly. Most machine learning methods proposed to that end approach this problem with supervised learning strategies in which known interactions are labeled as positive and the rest are labeled as negative. However, treating all unknown interactions as negative instances may lead to inaccuracies in real practice since some of the unknown interactions are bound to be positive interactions waiting to be identified as such. In this study, we propose to address this problem using the Quasi-Supervised Learning (QSL) algorithm. In this framework, potential interactions are predicted by estimating the overlap between a true positive dataset of compound-protein pairs with known interactions and an unknown dataset of all the remaining compound-protein pairs. The potential interactions are then identified as those in the unknown dataset that overlap with the interacting pairs in the true positive dataset in terms of the associated similarity structure. We also address the class-imbalance problem by modifying the conventional cost function of the QSL algorithm. Experimental results on GPCR and Nuclear Receptor datasets show that the proposed method can identify actual interactions from all possible combinations.en_US
dc.language.isoenen_US
dc.publisherWiley-VCH Verlagen_US
dc.relation.ispartofMolecular Informaticsen_US
dc.relation.urihttps://hdl.handle.net/11147/11684-
dc.rightsinfo:eu-repo/semantics/embargoedAccessen_US
dc.subjectMachine learningen_US
dc.subjectChemoinformaticsen_US
dc.subjectDrug discoveryen_US
dc.subjectCompound similarityen_US
dc.titleQuasi-supervised strategies for compound-protein interaction prediction [Article]en_US
dc.typeArticleen_US
dc.authorid0000-0002-5068-1356en_US
dc.authorid0000-0002-7765-6329en_US
dc.institutionauthorÇakı, Onuren_US
dc.institutionauthorKaraçalı, Bilgeen_US
dc.departmentİzmir Institute of Technology. Electrical and Electronics Engineeringen_US
dc.identifier.wosWOS:000722820300001en_US
dc.identifier.scopus2-s2.0-85119954344en_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.identifier.doi10.1002/minf.202100118-
dc.identifier.pmid34837345en_US
dc.contributor.affiliationIzmir Institute of Technologyen_US
dc.contributor.affiliationIzmir Institute of Technologyen_US
dc.relation.issn1868-1743en_US
dc.identifier.wosqualityQ1-
item.openairetypeArticle-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.fulltextWith Fulltext-
item.languageiso639-1en-
item.cerifentitytypePublications-
item.grantfulltextopen-
crisitem.author.dept03.05. Department of Electrical and Electronics Engineering-
Appears in Collections:Electrical - Electronic Engineering / Elektrik - Elektronik Mühendisliği
PubMed İndeksli Yayınlar Koleksiyonu / PubMed Indexed Publications Collection
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection
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