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Learning microRNA regulatory networks from genomic sequence and expression data

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MicroRNAs (miRNAs) regulate a large proportion of mammalian genes by hybridizing to targeted messenger RNAs (mRNAs) and down-regulating their translation into protein. Although much work has been done in the genome-wide computational prediction of miRNA genes and their target mRNAs, an open question is how to efficiently obtain functional miRNA targets from a large number of candidates. Here, I propose a novel Bayesian model and learning algorithm, GenMiR+ (Generative model for miRNA regulation), that accounts for patterns of gene expression using miRNA expression data and a set of candidate miRNA targets. A set of high-confidence functional miRNA targets are then obtained from the data using a variational Bayesian learning algorithm. The learning algorithm detects 467 functional miRNA targets out of 1, 770 targets obtained from TargetScanS in mouse at a false detection rate of 2.5%: several confirmed miRNA targets appear in our high-confidence set, such as the interaction miR-16 and BCL2 , an anti-apoptotic gene which has been implicated in chronic lymphocytic leukaemia. I will present results on the robustness of our model showing that the learning algorithm is not sensitive to various perturbations of the data. The set of GenMiR+ functional targets represent a significant increase in the number of miRNA targets and represent a starting point for a global understanding of gene regulation. Joint work with Quaid Morris and Brendan Frey.

This talk is part of the Microsoft Research Machine Learning and Perception Seminars series.

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