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Most computational methodologies for miRNA:mRNA target gene prediction use the seed segment of the miRNA and require cross-species sequence conservation in this region of the mRNA target. Methods that do not rely on conservation generate numbers of predictions, which are too large to validate. We describe a target prediction method (NBmiRTar) that does not require sequence conservation, using instead, machine learning by a nave Bayes classifier.

miRò is a web-based knowledge base that provides users with miRNA-phenotype associations in humans. It integrates data from various online sources, such as databases of miRNAs, ontologies, diseases and targets, into a unified database equipped with an intuitive and flexible query interface and data mining facilities. The main goal of miRò is the establishment of a knowledge base which allows non-trivial analysis through sophisticated mining techniques and the introduction of a new layer of associations between genes and phenotypes inferred based on miRNAs annotations.

MicroRNAs (miRNAs) are post-transcriptional regulators that bind to their target mRNAs through base complementarity. Predicting miRNA targets is a challenging task and various studies showed that existing algorithms suffer from high number of false predictions and low to moderate overlap in their predictions. Until recently, very few algorithms considered the dynamic nature of the interactions, including the effect of less specific interactions, the miRNA expression level, and the effect of combinatorial miRNA binding.

Short interfering RNAs (siRNAs) can be used to suppress gene expression and possess many potential applications in therapy, but how to design an effective siRNA is still not clear. Based on the MPI (Max-Planck-Institute) basic principles, a number of siRNA design tools have been developed recently. The set of candidates reported by these tools is usually large and often contains ineffective siRNAs. In view of this, we initiate the study of filtering ineffective siRNAs.

Breast cancer is a complex heterogeneous disease for which a substantial resource of transcriptomic data is available. Gene expression data have facilitated the division of breast cancer into, at least, five molecular subtypes, namely luminal A, luminal B, HER2, normal-like and basal. Once identified, breast cancer subtypes can inform clinical decisions surrounding patient treatment and prognosis. Indeed, it is important to identify patients at risk of developing aggressive disease so as to tailor the level of clinical intervention.

MicroRNAs (miRNAs) are small (19-24 nt), nonprotein-coding nucleic acids that regulate specific 'target' gene products via hybridization to mRNA transcripts, resulting in translational blockade or transcript degradation. Although miRNAs have been implicated in numerous developmental and adult diseases, their specific impact on biological pathways and cellular phenotypes, in addition to miRNA gene promoter regulation, remain largely unknown.