A novel biclustering algorithm for the discovery of meaningful biological correlations between miRNAs and mRNAs

microRNAs (miRNAs) are post-transcriptional regulators which represent one of the major regulatory gene families in animals, plants and viruses and that plays a key role in almost all main cellular processes. The computational prediction of miRNA target genes is important for the functional annotation of genomes and, on the other side, functional annotation of target genes can be of great help in suggesting specific biological functions of miRNAs [1]. This work aims to contribute to the elucidation of miRNAs role in the regulation of gene expression, by proposing a method for the hierarchical and overlapping biclustering of miRNAs and target messenger RNAs (mRNAs). The method allows to discover possible miRNA:mRNA functional relationships, at different granularity levels, in large datasets produced by miRNA target site prediction algorithms, thus reducing the impact of noise on the significance of the resulting biclusters.