A dynamic Bayesian framwork to learn temporal gene interactions using external knowledge

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dc.authorscopusid55260126200
dc.authorscopusid6602114582
dc.authorscopusid7006511521
dc.authorscopusid6603839786
dc.authorscopusid6602919034
dc.contributor.authorAğyüz, U.
dc.contributor.authorIşçi, S.
dc.contributor.authorÖztürk, C.
dc.contributor.authorAdemoğlu, A.
dc.contributor.authorOtu, H.H.
dc.date.accessioned2024-07-18T20:17:04Z
dc.date.available2024-07-18T20:17:04Z
dc.date.issued2013
dc.descriptionBSN Anatolia;European Commission;Fulbright;ODTU Teknokenten_US
dc.description2013 8th International Symposium on Health Informatics and Bioinformatics, HIBIT 2013 -- 25 September 2013 through 27 September 2013 -- Ankara -- 101984en_US
dc.description.abstractOne of the main problems in systems biology is learning gene interaction networks from experimental data. This turns out to be a challenging task as the experimental data is sparse and noisy, and network learning algorithms are computationally intense. Bayesian Networks (BN) have become a popular choice for learning such networks as BNs avoid overfitting and are robust to noise. In this paper we build up on our established framework, Bayesian Network Prior, where we incorporate existing biological knowledge in learning gene interaction networks. However, biological phenomena are time-dependent and there is need to extend the static structure of learning approaches to a temporal level. Here, we present a Dynamic BN framework, which learns interaction networks between different time points in time-series data. Both intra and inter networks are learnt and compared to standard DBN learning algorithms. Our results based on synthetic and simulated gene expression data suggest that the proposed method outperforms existing approaches in identifying the underlying network structure. The proposed framework is robust to errors in the incorporated knowledge and can combine various experimental data types together with existing knowledge when learning networks. © 2013 IEEE.en_US
dc.identifier.doi10.1109/HIBIT.2013.6661680
dc.identifier.isbn9781479907014
dc.identifier.scopus2-s2.0-84892630819en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.urihttps://doi.org/10.1109/HIBIT.2013.6661680
dc.identifier.urihttps://hdl.handle.net/11411/6397
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherIEEE Computer Societyen_US
dc.relation.ispartof2013 8th International Symposium on Health Informatics and Bioinformatics, HIBIT 2013en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectDynamic Bayesian Networksen_US
dc.subjectExternal Biological Knowledgeen_US
dc.subjectGene İnteraction Networksen_US
dc.subjectMicroarrayen_US
dc.subjectTime-Series Dataen_US
dc.subjectActive Networksen_US
dc.subjectBioinformaticsen_US
dc.subjectGene Expressionen_US
dc.subjectLearning Algorithmsen_US
dc.subjectMicroarraysen_US
dc.subjectBiological Phenomenaen_US
dc.subjectDynamic Bayesian Networksen_US
dc.subjectExternal Biological Knowledgeen_US
dc.subjectGene Expression Dataen_US
dc.subjectGene İnteraction Networksen_US
dc.subjectInteraction Networksen_US
dc.subjectNetwork Learning Algorithmsen_US
dc.subjectTime-Series Dataen_US
dc.subjectBayesian Networksen_US
dc.titleA dynamic Bayesian framwork to learn temporal gene interactions using external knowledge
dc.typeConference Object

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