Combining multiple clustering and network analysis for discoveries in gene expression data

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dc.contributor.authorAlhajj, Sleiman
dc.contributor.authorAlhajj, Aya
dc.contributor.authorTarıyan Özyer, Sibel
dc.date.accessioned2022-03-01T07:31:21Z
dc.date.available2022-03-01T07:31:21Z
dc.date.issued2021
dc.departmentİstanbul Medipol Üniversitesi, Uluslararası Tıp Fakültesi
dc.departmentİstanbul Medipol Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Biyomedikal Mühendisliği Bölümü
dc.description.abstractClustering is a challenging research task which could benefit a wide range of practical applications, including bioinformatics. It targets success by optimizing a number of objectives, a characteristic mostly ignored by clustering approaches. This paper describes a synthetic clustering algorithm which first applies multi-objective based approach to produce the alternative clustering solutions. Then the best clusters from each solution are selected and combined into a seed for a compact and effective solution which is expected to be better than all the individual solutions because it combines the best of each. This way, the developed algorithm may be classified as a fuzzy clustering approach because each object may belong to more than one cluster in the synthesized solution with a degree of membership in each cluster. Another interesting aspect of the algorithm is that it identifies the outliers. Further, a network is built from the relationships of the objects within the various clusters. The network is analyzed to reveal interesting discoveries not clearly reflected in the clustering outcome. The validity and applicability of the presented methodology has been assessed using synthetic and real data from the cancer.
dc.description.sponsorshipACM Special Interest Group on Knowledge Discovery in Data (SIGKDD) ; Elsevier ; IEEE Computer Society ; IEEE TCDE ; Springeren_US
dc.identifier.citationAlhajj, S., Alhajj, A. ve Tarıyan Özyer, S. (2021). Combining multiple clustering and network analysis for discoveries in gene expression data. 13th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM içinde (502-509. ss.). Virtual, Online, 8 November 2021. https://doi.org/10.1145/3487351.3490961
dc.identifier.doi10.1145/3487351.3490961
dc.identifier.endpage509
dc.identifier.isbn9781450391283
dc.identifier.scopus2-s2.0-85124414664
dc.identifier.scopusqualityN/A
dc.identifier.startpage502
dc.identifier.urihttps://doi.org/10.1145/3487351.3490961
dc.identifier.urihttps://hdl.handle.net/20.500.12511/9039
dc.indekslendigikaynakScopus
dc.institutionauthorAlhajj, Sleiman
dc.institutionauthorAlhajj, Aya
dc.institutionauthorTarıyan Özyer, Sibel
dc.language.isoen
dc.publisherAssociation for Computing Machinery, Inc
dc.relation.ispartof13th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAMen_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - İdari Personel ve Öğrenci
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectCancer Data Analysis
dc.subjectClustering
dc.subjectGene Expression Data
dc.subjectMulti-Objective Optimization
dc.subjectNetwork Analysis
dc.titleCombining multiple clustering and network analysis for discoveries in gene expression data
dc.typeConference Object

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