Gelişmiş Arama

Basit öğe kaydını göster

dc.contributor.authorOzisik, Ozan
dc.contributor.authorBakir-Gungor, Burcu
dc.contributor.authorDiri, Banu
dc.contributor.authorSezerman, Osman Ugur
dc.date.accessioned2021-08-23T10:56:52Z
dc.date.available2021-08-23T10:56:52Z
dc.date.issued2017en_US
dc.identifier.issn1574-8936
dc.identifier.issn2212-392X
dc.identifier.urihttps://doi.org/10.2174/1574893611666160527100444
dc.identifier.urihttps://hdl.handle.net/20.500.12573/929
dc.description.abstractBackground: A group of interconnected genes in a protein-protein interaction network that contains most of the disease associated genes is called an active subnetwork. Active subnetwork search is an NP-hard problem. In the last decade, simulated annealing, greedy search, color coding, genetic algorithm, and mathematical programming based methods are proposed for this problem. Method: In this study, we employed a novel genetic algorithm method for active subnetwork search problem. We used active node list chromosome representation, branch swapping crossover operator, multicombination of branches in crossover, mutation on duplicate individuals, pruning, and two stage genetic algorithm approach. The proposed method is tested on simulated datasets and Wellcome Trust Case Control Consortium rheumatoid arthritis genome-wide association study dataset. Our results are compared with the results of a simple genetic algorithm implementation and the results of the simulated annealing method that is proposed by Ideker et al. in their seminal paper. Results and Conclusion: The comparative study demonstrates that our genetic algorithm approach outperforms the simple genetic algorithm implementation in all datasets and simulated annealing in all but one datasets in terms of obtained scores, although our method is slower. Functional enrichment results show that the presented approach can successfully extract high scoring subnetworks in simulated datasets and identify significant rheumatoid arthritis associated subnetworks in the real dataset. This method can be easily used on the datasets of other complex diseases to detect disease-related active subnetworks. Our implementation is freely available at https://www.ce.yildiz.edu.tr/personal/ozanoz/file/6611/ActSubGAen_US
dc.language.isoengen_US
dc.publisherBENTHAM SCIENCE PUBL LTDEXECUTIVE STE Y-2, PO BOX 7917, SAIF ZONE, 1200 BR SHARJAH, U ARAB EMIRATESen_US
dc.relation.isversionof10.2174/1574893611666160527100444en_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectrheumatoid arthritisen_US
dc.subjectGWASen_US
dc.subjectgenetic algorithmen_US
dc.subjectdysfunctional pathwayen_US
dc.subjectdisease associated moduleen_US
dc.subjectActive subnetwork searchen_US
dc.titleActive Subnetwork GA: A Two Stage Genetic Algorithm Approach to Active Subnetwork Searchen_US
dc.typearticleen_US
dc.contributor.departmentAGÜ, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümüen_US
dc.contributor.institutionauthorBakir-Gungor, Burcu
dc.identifier.volumeVolume 12 Issue 4 Page 320-328en_US
dc.relation.journalCURRENT BIOINFORMATICSen_US
dc.relation.publicationcategoryMakale - Uluslararası - Editör Denetimli Dergien_US


Bu öğenin dosyaları:

DosyalarBoyutBiçimGöster

Bu öğe ile ilişkili dosya yok.

Bu öğe aşağıdaki koleksiyon(lar)da görünmektedir.

Basit öğe kaydını göster