Please use this identifier to cite or link to this item: https://hdl.handle.net/11147/12267
Full metadata record
DC FieldValueLanguage
dc.contributor.authorTenekeci, Sameten_US
dc.contributor.authorIşık, Zerrinen_US
dc.date.accessioned2022-08-05T08:27:57Z-
dc.date.available2022-08-05T08:27:57Z-
dc.date.issued2022-
dc.identifier.urihttps://doi.org/10.1109/TCBB.2020.2993301-
dc.identifier.urihttps://hdl.handle.net/11147/12267-
dc.description.abstractIdentification of common molecular mechanisms in interrelated diseases is essential for better prognoses and targeted therapies. However, complexity of metabolic pathways makes it difficult to discover common disease genes underlying metabolic disorders; and it requires more sophisticated bioinformatics models that combine different types of biological data and computational methods. Accordingly, we built an integrative network analysis model to identify shared disease genes in metabolic syndrome (MS), type 2 diabetes (T2D), and coronary artery disease (CAD). We constructed weighted gene co-expression networks by combining gene expression, protein-protein interaction, and gene ontology data from multiple sources. For 90 different configurations of disease networks, we detected the significant modules by using MCL, SPICi, and Linkcomm graph clustering algorithms. We also performed a comparative evaluation on disease modules to determine the best method providing the highest biological validity. By overlapping the disease modules, we identified 22 shared genes for MS-CAD and T2D-CAD. Moreover, 19 out of these genes were directly or indirectly associated with relevant diseases in the previous medical studies. This study does not only demonstrate the performance of different biological data sources and computational methods in disease-gene discovery, but also offers potential insights into common genetic mechanisms of the metabolic disorders.en_US
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineersen_US
dc.relation.ispartofIEEE/ACM Transactions on Computational Biology and Bioinformaticsen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectCoronary artery diseaseen_US
dc.subjectGene expressionen_US
dc.subjectGene ontologyen_US
dc.subjectMetabolic syndromeen_US
dc.titleIntegrative biological network analysis to identify shared genes in metabolic disordersen_US
dc.typeArticleen_US
dc.authorid0000-0001-8875-4111en_US
dc.authoridWOS:000752015800051-
dc.institutionauthorTenekeci, Sameten_US
dc.departmentİzmir Institute of Technology. Computer Engineeringen_US
dc.identifier.wosWOS:000752015800051en_US
dc.identifier.scopus2-s2.0-85124054886en_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.identifier.doi10.1109/TCBB.2020.2993301-
dc.identifier.pmid32396100-
dc.contributor.affiliation01. Izmir Institute of Technologyen_US
dc.contributor.affiliationDokuz Eylül Üniversitesien_US
dc.relation.issn1545-5963en_US
dc.description.volume19en_US
dc.description.issue1en_US
dc.description.startpage522en_US
dc.description.endpage530en_US
dc.identifier.scopusqualityQ2-
item.openairetypeArticle-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.grantfulltextopen-
item.fulltextWith Fulltext-
item.languageiso639-1en-
item.cerifentitytypePublications-
crisitem.author.dept01. Izmir Institute of Technology-
Appears in Collections:Computer Engineering / Bilgisayar 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
Files in This Item:
File Description SizeFormat 
Integrative_Biological_Network_Analysis_to_Identify.pdfArticle604.46 kBAdobe PDFView/Open
Show simple item record



CORE Recommender

SCOPUSTM   
Citations

1
checked on Apr 5, 2024

WEB OF SCIENCETM
Citations

2
checked on Mar 23, 2024

Page view(s)

1,468
checked on Apr 8, 2024

Download(s)

104
checked on Apr 8, 2024

Google ScholarTM

Check




Altmetric


Items in GCRIS Repository are protected by copyright, with all rights reserved, unless otherwise indicated.