Isci, S.Agyuz, U.Ozturk, C.Otu, H.H.2024-07-182024-07-1820129781467308786https://doi.org/10.1109/HIBIT.2012.6209047https://hdl.handle.net/11411/6396Middle East Technical University (METU);Inst. Electr. Electron. Eng. (IEEE) Eng. Med. Biol. Soc. (EMBS);TUBITAK;British Council;AKGUN Yazilim2012 7th International Symposium on Health Informatics and Bioinformatics, HIBIT 2012 -- 19 April 2012 through 22 April 2012 -- Cappadocia -- 90667Biological 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.eninfo:eu-repo/semantics/closedAccessBayesian Network ModelsBiogridBiological DataCandidate NetworkClinical DatabaseExperimental DataExternal KnowledgeGene İnteractionsHigh RateHigh ThroughputInteracting GenesInteraction DatabaseLearning NetworkNetwork GenerationParameter LearningPrior KnowledgeStructure-LearningSynthetic And Real DataBioinformaticsComputer SimulationForecastingGenesProteinsBayesian NetworksConference Object2-s2.0-8486272676210.1109/HIBIT.2012.620904787N/A82