Investigation of Association Estimators in Network Inference Algorithms on Breast Cancer Proteomic Data

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dc.authorid0000-0001-5495-7754
dc.authorid0000-0003-3186-8091
dc.authorwosidErdoğan, Cihat/E-4681-2019
dc.authorwosidDiri, Banu/AAA-1020-2021
dc.contributor.authorErdoğan, Cihat
dc.contributor.authorKurt, Zeyneb
dc.contributor.authorDiri, Banu
dc.date.accessioned2022-05-11T14:15:51Z
dc.date.available2022-05-11T14:15:51Z
dc.date.issued2017
dc.departmentFakülteler, Çorlu Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü
dc.description25th Signal Processing and Communications Applications Conference (SIU) -- MAY 15-18, 2017 -- Antalya, TURKEY
dc.description.abstractIn this study, association estimators applied in the network inference methods used to determine disease-related molecular interactions using breast cancer, which is the most common type of cancer in women, proteomic data were examined and hub genes in the gene-gene interaction network related to the disease were identified. Proteomic data of 901 breast cancer patients were generated using reverse phase protein array provided by The Cancer Proteome Atlas (TCPA) as a data set. Correlations and mutual information (MI) based estimators used in the literature were compared in the study, and WGCNA and minet R packages were used. As a result, it is seen that the MI based shrink estimator method has more successful results than the correlation-based adjacency function used in the estimation of biological networks in the WGCNA package. Achievement rates have ranged from 0.67 to 1.00 in the shrink estimation, with adjacency functions ranging from 0.33 to 0.86 for different module counts. In addition, hub genes and inferenced networks of successful results arc presented for the review of biologists.
dc.description.sponsorshipTurk Telekom, Arcelik A S, Aselsan, ARGENIT, HAVELSAN, NETAS, Adresgezgini, IEEE Turkey Sect, AVCR Informat Technologies, Cisco, i2i Syst, Integrated Syst & Syst Design, ENOVAS, FiGES Engn, MS Spektral, Istanbul Teknik Univ
dc.identifier.isbn978-1-5090-6494-6
dc.identifier.issn2165-0608
dc.identifier.scopus2-s2.0-85026292149
dc.identifier.scopusqualityN/A
dc.identifier.urihttps://hdl.handle.net/20.500.11776/6095
dc.identifier.wosWOS:000413813100533
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorErdoğan, Cihat
dc.language.isotr
dc.publisherIEEE
dc.relation.ispartof2017 25th Signal Processing and Communications Applications Conference (Siu)
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectassociation estimators
dc.subjectnetwork inference
dc.subjectproteomic
dc.subjectbreast cancer
dc.titleInvestigation of Association Estimators in Network Inference Algorithms on Breast Cancer Proteomic Data
dc.typeConference Object

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