LBSA-DRIVER: A Novel Approach to Identify Cancer Driver Genes Using List-Based Simulated Annealing

Basit Öğe Kaydı
dc.authorid0000-0003-4469-1440
dc.authorid0000-0002-3298-3334
dc.contributor.authorAtay, Yilmaz
dc.contributor.authorNgobesing, Lionel Alangeh
dc.contributor.authorCingiz, Mustafa Ozgur
dc.date.accessioned2026-02-08T15:15:52Z
dc.date.available2026-02-08T15:15:52Z
dc.date.issued2025
dc.departmentBursa Teknik Üniversitesi
dc.description.abstractIntroduction Cancer driver genes are genes responsible for cancer genesis; thus, identifying cancer-related genes is crucial in fostering cancer treatment. The accuracy in identifying cancer driver genes within the vast pool of normal passenger genes directly influences the efficacy of treatment approaches.Objective This research aimed to effectively identify cancer driver genes using the List-based Simulated Annealing (LBSA) optimization technique.Method The proposed model (LBSA-DRIVER) harnesses a list-based simulated annealing algorithm within a bipartite network to pinpoint cancer driver genes. The process begins with creating a bipartite graph that integrates gene mutations and expression data from carefully chosen datasets. The LBSA algorithm is then applied to the generated graph to identify and rank the genes, drawing insights from a biological interaction network.Result Following the algorithm's development, rigorous experimental analyses have been conducted using four benchmark datasets from The Cancer Genome Atlas (TCGA) database. The datasets used were the Breast Cancer dataset (BRCA), Prostate Adenocarcinoma dataset (PRAD), Ovarian cancer dataset (OV), and Glioblastoma Multiforme dataset (GBM).Conclusion Our findings, including precision, recall, F-score, and accuracy metrics, provide strong evidence of the effectiveness of the proposed model in identifying driver genes.
dc.description.sponsorshipDeclared none.
dc.identifier.doi10.2174/0115748936302984240604061302
dc.identifier.endpage358
dc.identifier.issn1574-8936
dc.identifier.issn2212-392X
dc.identifier.issue4
dc.identifier.scopus2-s2.0-105004256771
dc.identifier.scopusqualityQ1
dc.identifier.startpage344
dc.identifier.urihttps://doi.org/10.2174/0115748936302984240604061302
dc.identifier.urihttps://hdl.handle.net/20.500.12885/5998
dc.identifier.volume20
dc.identifier.wosWOS:001274831200001
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherBentham Science Publ Ltd
dc.relation.ispartofCurrent Bioinformatics
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzWOS_KA_20260207
dc.subjectCancer
dc.subjectdriver genes
dc.subjectgene ontology
dc.subjectlist-based simulated annealing
dc.subjectbiological interactions
dc.subjecttranscriptional networks
dc.titleLBSA-DRIVER: A Novel Approach to Identify Cancer Driver Genes Using List-Based Simulated Annealing
dc.typeArticle

Dosyalar

Koleksiyon

WoS İndeksli Yayınlar Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu