Estimation of the proteomic cancer co-expression sub networks by using association estimators

Basit Öğe Kaydı
dc.authorid0000-0001-5495-7754
dc.authorid0000-0001-5495-7754
dc.authorid0000-0003-3186-8091
dc.authorid0000-0002-6652-4339
dc.authorscopusid56038664700
dc.authorscopusid16230879200
dc.authorscopusid22978771800
dc.authorwosidDiri, Banu/AAA-1020-2021
dc.authorwosidErdoğan, Cihat/A-4856-2018
dc.authorwosidErdoğan, Cihat/E-4681-2019
dc.contributor.authorErdoğan, Cihat
dc.contributor.authorKurt, Zeyneb
dc.contributor.authorDiri, Banu
dc.date.accessioned2022-05-11T14:15:50Z
dc.date.available2022-05-11T14:15:50Z
dc.date.issued2017
dc.departmentFakülteler, Çorlu Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü
dc.description.abstractIn this study, the association estimators, which have significant influences on the gene network inference methods and used for determining the molecular interactions, were examined within the co-expression network inference concept. By using the proteomic data from five different cancer types, the hub genes/proteins within the disease-associated gene-gene/protein-protein interaction sub networks were identified. Proteomic data from various cancer types is collected from The Cancer Proteome Atlas (TCPA). Correlation and mutual information (MI) based nine association estimators that are commonly used in the literature, were compared in this study. As the gold standard to measure the association estimators' performance, a multi-layer data integration platform on gene-disease associations (DisGeNET) and the Molecular Signatures Database (MSigDB) was used. Fisher's exact test was used to evaluate the performance of the association estimators by comparing the created co-expression networks with the disease-associated pathways. It was observed that the MI based estimators provided more successful results than the Pearson and Spearman correlation approaches, which are used in the estimation of biological networks in the weighted correlation network analysis (WGCNA) package. In correlation-based methods, the best average success rate for five cancer types was 60%, while in MI-based methods the average success ratio was 71% for James-Stein Shrinkage (Shrink) and 64% for Schurmann-Grassberger (SG) association estimator, respectively. Moreover, the hub genes and the inferred sub networks are presented for the consideration of researchers and experimentalists.
dc.identifier.doi10.1371/journal.pone.0188016
dc.identifier.issn1932-6203
dc.identifier.issue11en_US
dc.identifier.pmid29145449
dc.identifier.scopus2-s2.0-85034219224
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1371/journal.pone.0188016
dc.identifier.urihttps://hdl.handle.net/20.500.11776/6093
dc.identifier.volume12
dc.identifier.wosWOS:000415378800051
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.institutionauthorErdoğan, Cihat
dc.language.isoen
dc.publisherPublic Library Science
dc.relation.ispartofPlos One
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectMutual Information
dc.subjectGene-Expression
dc.subjectInference
dc.subjectEntropy
dc.titleEstimation of the proteomic cancer co-expression sub networks by using association estimators
dc.typeArticle

Dosyalar

Orijinal paket
Listeleniyor 1 - 1 / 1
Küçük Resim
İsim:
6093.pdf
Boyut:
11.28 MB
Biçim:
Adobe Portable Document Format
Açıklama:
Tam Metin / Full Text
İndir

Koleksiyon

WoS İndeksli Yayınlar Koleksiyonu
PubMed İndeksli Yayın Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu
Çorlu Mühendislik Fakültesi Koleksiyonu