Accurate prediction of human miRNA targets via graph modeling of the miRNA-target duplex

miRNAs are involved in many critical cellular activities through binding to their mRNA targets, e.g. in cell proliferation, differentiation, death, growth control, and developmental timing. Accurate prediction of miRNA targets can assist efficient experimental investigations on the functional roles of miRNAs. Their prediction, however, remains a challengeable task due to the lack of experimental data about the tertiary structure of miRNA-target binding duplexes. In particular, correlations of nucleotides in the binding duplexes may not be limited to the canonical Watson Crick base pairs (BPs) as they have been perceived; methods based on secondary structure prediction (typically minimum free energy (MFE)) have only had mix success. In this work, we characterized miRNA binding duplexes with a graph model to capture the correlations between pairs of nucleotides of an miRNA and its target sequences. We developed machine learning algorithms to train the graph model to predict the target sites of miRNAs. In pa...