Fruitfly

The computational prediction of novel microRNA within a full genome involves identifying sequences having the highest chance of being a miRNA precursor (pre-miRNA). These sequences are usually named candidates to miRNA. The well-known pre-miRNAs are usually only a few in comparison to the hundreds of thousands of potential candidates to miRNA that have to be analyzed, which makes this task a high classimbalance classification problem.

The double helix is a conformation that genomic DNA usually assumes; under certain conditions, however, guanine-rich DNA sequences can form a four-stranded structure, G-quadruplex, which is found to play a role in regulating gene expression. Indeed, it has been demonstrated that the G-quadruplex formed in the c-MYC promoter suppresses its transcriptional activity. Recent studies suggest that G-quadruplex motifs (GQMs) are enriched in human gene promoters.

We used a machine learning method, the nearest neighbor algorithm (NNA), to learn the relationship between miRNAs and their target proteins, generating a predictor which can then judge whether a new miRNA-target pair is true or not. We acquired 198 positive (true) miRNA-target pairs from Tarbase and the literature, and generated 4,888 negative (false) pairs through random combination. A 0/1 system and the frequencies of single nucleotides and di-nucleotides were used to encode miRNAs into vectors while various physicochemical parameters were used to encode the targets.