Detecting gene interactions within a Bayesian Network framework using external knowledge

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dc.authorscopusid6602114582
dc.authorscopusid55260126200
dc.authorscopusid7006511521
dc.authorscopusid6602919034
dc.contributor.authorIsci, S.
dc.contributor.authorAgyuz, U.
dc.contributor.authorOzturk, C.
dc.contributor.authorOtu, H.H.
dc.date.accessioned2024-07-18T20:17:04Z
dc.date.available2024-07-18T20:17:04Z
dc.date.issued2012
dc.descriptionMiddle East Technical University (METU);Inst. Electr. Electron. Eng. (IEEE) Eng. Med. Biol. Soc. (EMBS);TUBITAK;British Council;AKGUN Yazilimen_US
dc.description2012 7th International Symposium on Health Informatics and Bioinformatics, HIBIT 2012 -- 19 April 2012 through 22 April 2012 -- Cappadocia -- 90667en_US
dc.description.abstractBiological and clinical databases are increasing at a very high rate making a large volume of experimental data publicly available. In this paper, we propose a framework that makes use of external biological knowledge to predict if two given genes interact with each other. To this end, we utilize prior knowledge about interaction of two genes by generating a Bayesian Network using existing external biological knowledge. External knowledge types to be utilized are obtained from interaction databases such as BioGrid and Reac-tome and consist of protein-protein, protein-DNA/RNA, and gene interactions. We first built a naïve Bayesian Network to predict if two genes interact by employing parameter learning using known gene interactions. We propose that the resulting model will be incorporated into methods learning networks from high throughput biological data and interacting genes will be represented in the form of a network. In this process of network generation, the Bayesian Network model deducing gene interactions from external knowledge will be used to calculate the probability of candidate networks to enhance the structure learning task. Our simulations on both synthetic and real data sets show that proposed framework can successfully enhance identification of the true network and be used in predicting gene interactions. © 2012 IEEE.en_US
dc.identifier.doi10.1109/HIBIT.2012.6209047
dc.identifier.endpage87en_US
dc.identifier.isbn9781467308786
dc.identifier.scopus2-s2.0-84862726762en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.startpage82en_US
dc.identifier.urihttps://doi.org/10.1109/HIBIT.2012.6209047
dc.identifier.urihttps://hdl.handle.net/11411/6396
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.relation.ispartof2012 7th International Symposium on Health Informatics and Bioinformatics, HIBIT 2012en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectBayesian Network Modelsen_US
dc.subjectBiogriden_US
dc.subjectBiological Dataen_US
dc.subjectCandidate Networken_US
dc.subjectClinical Databaseen_US
dc.subjectExperimental Dataen_US
dc.subjectExternal Knowledgeen_US
dc.subjectGene İnteractionsen_US
dc.subjectHigh Rateen_US
dc.subjectHigh Throughputen_US
dc.subjectInteracting Genesen_US
dc.subjectInteraction Databaseen_US
dc.subjectLearning Networken_US
dc.subjectNetwork Generationen_US
dc.subjectParameter Learningen_US
dc.subjectPrior Knowledgeen_US
dc.subjectStructure-Learningen_US
dc.subjectSynthetic And Real Dataen_US
dc.subjectBioinformaticsen_US
dc.subjectComputer Simulationen_US
dc.subjectForecastingen_US
dc.subjectGenesen_US
dc.subjectProteinsen_US
dc.subjectBayesian Networksen_US
dc.titleDetecting gene interactions within a Bayesian Network framework using external knowledge
dc.typeConference Object

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