Tip 2 Diyabet'te Etkilenen Yolak Alt Ağlarını Bulmak İçin Yukarıdan Aşağıya İşleyen Bir Yaklaşım

dc.contributor.author Unlu Yazici, Miray
dc.contributor.author Bakir-Gungor, Burcu
dc.date.accessioned 2025-09-25T10:37:01Z
dc.date.available 2025-09-25T10:37:01Z
dc.date.issued 2020
dc.description Unlu Yazici, Miray/0000-0001-8165-6164 en_US
dc.description.abstract Diabetes Mellitus (DM) is a metabolic disorder caused by dysfunction of insulin-producing pancreatic beta cells, insulin resistance, or impairment of insulin functionality. Type 2 Diabetes Mellitus (T2D) is a complex multifactorial disease that accounts for 90% of diabetes cases. In recent years, genome-wide association studies (GWAS) have successfully identified genetic variants associated with T2D risk. However, while conventional GWAS analyses focus on 'the tip of the iceberg' single nucleotide polymorphisms (SNPs), new analysis methods are needed to uncover hidden variations in these studies. In our previous study, we developed a post-GWAS analysis methodology to find disease-associated marker pathways by integrating human protein-protein interaction network, known biological pathways and potential SNPs. In this study, via adding different in-silico approaches to our methodology, we aim to identify affected pathway subnetworks and affected pathway clusters in addition to the affected protein subnetworks in T2D, and consequently to enlighten molecular mechanisms of T2D. Using this proposed method, we analyzed T2D GWAS meta-analysis data including 12.931 cases ye 57.196 controls. The approach we presented here is based on both the significance value of affected pathway and its topological relationship with other neighbor pathways. In the functional enrichment stage of our method, important pathways were obtained using hypergeometric test and gene-pathway matrix was formed. Then pathway-pathway similarity values were calculated using Jaccard index. Using the scores obtained in the similarity matrix, pathway-pathway network was constructed, and disease-related pathway modules were obtained using subnetwork search algorithms. As a result, genes, pathways and pathway subnetworks that might have a potential role in T2D development were identified, and the categories and classes that are related with these affected pathways were determined. en_US
dc.identifier.doi 10.1109/SIU49456.2020.9302299
dc.identifier.isbn 9781728172064
dc.identifier.issn 2165-0608
dc.identifier.scopus 2-s2.0-85100303589
dc.identifier.uri https://doi.org/10.1109/SIU49456.2020.9302299
dc.identifier.uri https://hdl.handle.net/20.500.12573/2907
dc.language.iso tr en_US
dc.publisher IEEE en_US
dc.relation.ispartof 28th Signal Processing and Communications Applications Conference (SIU) -- OCT 05-07, 2020 -- ELECTR NETWORK en_US
dc.relation.ispartofseries Signal Processing and Communications Applications Conference
dc.rights info:eu-repo/semantics/closedAccess en_US
dc.subject Single Nucleotide Polymorphism (Snp) en_US
dc.subject Genomewide Association Study (GWAS) en_US
dc.subject Pathway Subnetwork en_US
dc.subject Type 2 Diabetes en_US
dc.title Tip 2 Diyabet'te Etkilenen Yolak Alt Ağlarını Bulmak İçin Yukarıdan Aşağıya İşleyen Bir Yaklaşım en_US
dc.title.alternative A Top-Down Approach for Finding Affected Pathway Subnetworks in Type 2 Diabetes en_US
dc.type Conference Object en_US
dspace.entity.type Publication
gdc.author.id Unlu Yazici, Miray/0000-0001-8165-6164
gdc.author.scopusid 56305651300
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gdc.author.wosid Unlu Yazici, Miray/Hji-9236-2023
gdc.bip.impulseclass C5
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gdc.bip.popularityclass C5
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gdc.collaboration.industrial false
gdc.description.department Abdullah Gül University en_US
gdc.description.departmenttemp [Unlu Yazici, Miray] Abdullah Gul Univ, Biyomuhendislik, Yasam & Doga Bilimleri Fak, Kayseri, Turkey; [Bakir-Gungor, Burcu] Abdullah Gul Univ, Bilgisayar Muhendisligi, Muhendislik Fak, Kayseri, Turkey en_US
gdc.description.endpage 4
gdc.description.publicationcategory Konferans Öğesi - Uluslararası - Kurum Öğretim Elemanı en_US
gdc.description.scopusquality N/A
gdc.description.startpage 1
gdc.description.woscitationindex Conference Proceedings Citation Index - Science
gdc.description.wosquality N/A
gdc.identifier.openalex W3119352102
gdc.identifier.wos WOS:000653136100273
gdc.index.type WoS
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gdc.oaire.isgreen false
gdc.oaire.popularity 1.3503004E-9
gdc.oaire.publicfunded false
gdc.oaire.sciencefields 0301 basic medicine
gdc.oaire.sciencefields 03 medical and health sciences
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gdc.openalex.normalizedpercentile 0.13
gdc.opencitations.count 0
gdc.plumx.mendeley 3
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gdc.virtual.author Ünlü Yazıcı, Miray
gdc.virtual.author Güngör, Burcu
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