Human

Contemporary molecular biology deals with wide and heterogeneous sets of measurements to model and understand underlying biological processes including complex diseases. Machine learning provides a frequent approach to build such models. However, the models built solely from measured data often suffer from overfitting, as the sample size is typically much smaller than the number of measured features. In this paper, we propose a random forest-based classifier that reduces this overfitting with the aid of prior knowledge in the form of a feature interaction network.

The recent discovery of the first small modulatory RNA (smRNA) presents the challenge of finding other molecules of similar length and conservation level. Unlike short interfering RNA (siRNA) and micro-RNA (miRNA), effective computational and experimental screening methods are not currently known for this species of RNA molecule, and the discovery of the one known example was partly fortuitous because it happened to be complementary to a well-studied DNA binding motif (the Neuron Restrictive Silencer Element).

With the rapid accumulation of microRNA (miRNAs), a class of newly identified small noncoding RNAs, in silico inference of miRNA functions has become one of the central tasks in miRNA bioinformatics. Traditional methods have helped in the understanding of miRNAs, but they also have limitations.

Small RNA sequencing is the most widely used tool for microRNA (miRNA) discovery, and shows great potential for the efficient study of miRNA cross-species transport, i.e., by detecting the presence of exogenous miRNA sequences in the host species. Because of the increased appreciation of dietary miRNAs and their far-reaching implication in human health, research interests are currently growing with regard to exogenous miRNAs bioavailability, mechanisms of cross-species transport and miRNA function in cellular biological processes.