Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/7403
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dc.contributor.authorAlshalalfa, Mohammed-
dc.contributor.authorTan, Mehmet-
dc.contributor.authorNaji, Ghada-
dc.contributor.authorAlhajj, Reda-
dc.contributor.authorPolat, Faruk-
dc.contributor.authorRokne, Jon-
dc.date.accessioned2021-09-11T15:56:49Z-
dc.date.available2021-09-11T15:56:49Z-
dc.date.issued2012en_US
dc.identifier.issn1094-6977-
dc.identifier.issn1558-2442-
dc.identifier.urihttps://doi.org/10.1109/TSMCC.2012.2186801-
dc.identifier.urihttps://hdl.handle.net/20.500.11851/7403-
dc.description.abstractThe past decades have witnessed advances in genomic technology; and this has allowed laboratories to generate vast amount of biological data, including microarray gene expression data. Effective analysis of the data helps in better understanding the mechanisms behind the complex behavior of the cell. Actually, a huge body of research focuses on the role of gene regulatory networks (GRNs) in controlling the cell. However, studying the heterogeneous interactions between mRNA and miRNA has received less attention. Fortunately, revealing the targets of miRNAs started to gain some consideration from the research community. Further, integrating mRNA gene expression and miRNA expression data is receiving more attention; the target is to understand the role of miRNA in regulating mRNA in different cell contexts; this could lead to predicting miRNA targets and constructing miRNA-mRNA interaction networks. On the other hand, we have already demonstrated the power of constraint-based learning as a promising technique to learn the structure of GRN[37], which are homogeneous in the sense that they contain one type of nodes, namely, genes. In this study, we extend our previous work to show how constraint-based learning can be effectively applied to tackle a more challenging problem, namely, to learn the structure of heterogeneous networks, like mRNA-miRNA network. In other words, to build the whole picture of the heterogeneous interactions, we used constraint-based learning algorithms which usually perform well on sparse graphs to predict the interactions within heterogeneous networks, namely, miRNA-mRNA interactions. We are able to achieve this by extending our PCPDPr algorithm, which works on homogeneous networks. The extended version named htrPCPDPr is capable of handling networks connecting two heterogeneous sets of nodes into a bipartite graph. This way, we propose a new learning mechanism to predict miRNA targets from expression profiles of both mRNA and miRNA, in addition to sequence-based prior knowledge about the interactions. The method has been applied to different set of genes related to the Alzheimer disease; the results reported in this paper demonstrate the novelty, applicability, and effectiveness of the proposed approach.en_US
dc.language.isoenen_US
dc.publisherIEEE-Inst Electrical Electronics Engineers Incen_US
dc.relation.ispartofIEEE Transactions On Systems Man And Cybernetics Part C-Applications And Reviewsen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectConstraint-based learningen_US
dc.subjectexpression profilesen_US
dc.subjectgene regulationen_US
dc.subjectinteraction networksen_US
dc.subjectmicroRNAen_US
dc.subjectmiRNA regulationen_US
dc.titleRevealing miRNA Regulation and miRNA Target Prediction Using Constraint-Based Learningen_US
dc.typeArticleen_US
dc.departmentFaculties, Faculty of Engineering, Department of Computer Engineeringen_US
dc.departmentFakülteler, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümütr_TR
dc.identifier.volume42en_US
dc.identifier.issue6en_US
dc.identifier.startpage1354en_US
dc.identifier.endpage1364en_US
dc.authorid0000-0003-0509-9153-
dc.authorid0000-0002-1741-0570-
dc.identifier.wosWOS:000312885400018en_US
dc.identifier.scopus2-s2.0-84871719870en_US
dc.institutionauthorTan, Mehmet-
dc.identifier.doi10.1109/TSMCC.2012.2186801-
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.grantfulltextnone-
item.fulltextNo Fulltext-
item.openairetypeArticle-
item.cerifentitytypePublications-
item.languageiso639-1en-
crisitem.author.dept02.3. Department of Computer Engineering-
Appears in Collections:Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection
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