Inferring Disease-Related Pathways Using A Probabilistic Epistasis Model

TitleInferring Disease-Related Pathways Using A Probabilistic Epistasis Model
Publication TypeConference Paper
Year of Publication2009
AuthorsKanabar, PN, Vaske, CJ, Yeang, CH, Yildiz, FH, Stuart, JM
Conference NamePacific Symposium on Biocomputing
AbstractMotivation: We present a probabilistic model called a Joint Intervention Network (JIN) for inferring interactions among a chosen set of regulator genes. The input to the method are expression changes of downstream indicator genes observed under the knock-out of the regulators. JIN can use any number of perturbation combinations for model inference (e.g. single, double, and triple knock-outs). Results/Conclusions: We applied JIN to a Vibrio cholerae regulatory network to uncover mechanisms critical to its environmental persistence. V. cholerae is a facultative human pathogen that causes cholera in humans and responsible for seven pandemics. We analyzed the expression response of 17 V. cholerae biofilm indicator genes under various single and multiple knock-outs of three known biofilm regulators. Using the inferred network, we were able to identify new genes involved in biofilm formation more accurately than clustering expression profiles.
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