A view of the EM algorithm that justifies incremental, sparse, and other variants
Proceedings of the NATO Advanced Study Institute on Learning in graphical models
An introduction to variational methods for graphical models
Learning in graphical models
A high-throughput approach for associating microRNAs with their activity conditions
RECOMB'05 Proceedings of the 9th Annual international conference on Research in Computational Molecular Biology
Discovery of functional miRNA-mRNA regulatory modules with computational methods
Journal of Biomedical Informatics
Detecting microarray data supported microRNA-mRNA interactions
International Journal of Data Mining and Bioinformatics
Computers in Biology and Medicine
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MicroRNAs (miRNAs) have recently been discovered as an important class of non-coding RNA genes that play a major role in regulating gene expression, providing a means to control the relative amounts of mRNA transcripts and their protein products. Although much work has been done in the genome-wide computational prediction of miRNA genes and their target mRNAs, two open questions are how miRNAs regulate gene expression and how to efficiently detect bona fide miRNA targets from a large number of candidate miRNA targets predicted by existing computational algorithms. In this paper, we present evidence that miRNAs function by post-transcriptional degradation of mRNA target transcripts: based on this, we propose a novel probabilistic model that accounts for gene expression using miRNA expression data and a set of candidate miRNA targets. A set of underlying miRNA targets are learned from the data using our algorithm, GenMiR (Generative model for miRNA regulation). Our model scores and detects 601 out of 1,770 targets obtained from TargetScanS in mouse at a false detection rate of 5%. Our high-confidence miRNA targets include several which have been previously validated by experiment: the remainder potentially represent a dramatic increase in the number of known miRNA targets.