资 源 简 介
Motivation: MicroRNAs have recently emerged as a major class of
regulatory molecules involved in a broad range of biological
processes and complex diseases. Construction of miRNA-target
regulatory networks can provide useful information for the study and
diagnosis of complex diseases. Many sequence-based and
evolutionary information-based methods have been developed to
identify miRNA-mRNA targeting relationships. However, as the
amount of available miRNA and gene expression data grows, a
more statistical and systematic method combining sequence-based
binding predictions and expression-based correlation data becomes
necessary for the accurate identification of miRNA-mRNA pairs.
Results: We propose a Lasso regression model for the identification
of miRNA-mRNA targeting relationships that combines sequencebased
prediction information, miRNA co-regulation, RISC availability,
and miRNA/mRNA abundance data. By comparing this modelling
approach wit
