Integrative Approaches for microRNA Target Prediction: Combining Sequence Information and the Paired mRNA and miRNA Expression Profiles
Naifang Su, Minping Qian and Minghua Deng
Pages 37-45 (9)
Gene regulation is a key factor in gaining a full understanding of molecular biology. microRNA (miRNA), a
novel class of non-coding RNA, has recently been found to be one crucial class of post-transactional regulators, and play
important roles in cancer. One essential step to understand the regulatory effect of miRNAs is the reliable prediction of
their target mRNAs. Typically, the predictions are solely based on the sequence information, which unavoidably have
high false detection rates. Recently, some novel approaches are developed to predict miRNA targets by integrating the
typical algorithm with the paired expression profiles of miRNA and mRNA. Here we review and discuss these integrative
approaches and propose a new algorithm called HCTarget. Applying HCtarget to the expression data in multiple
myeloma, we predict target genes for ten specific miRNAs. The experimental verification and a loss of function study
validate our predictions. Therefore, the integrative approach is a reliable and effective way to predict miRNA targets, and
could improve our comprehensive understanding of gene regulation.
Expression profile, integrative analysis, miRNA, target prediction, Gene regulation, molecular biology, cancer, BAYESIAN NETWORK APPROACH, STATISTICAL INFERENCE, ONTOLOGY ENRICHMENT ANALYSIS
LMAM, School of Mathematical Sciences, Peking University, Beijing 100871, China.