Probabilistic neural network inferences on oligonucleotide classification based on Oligo: Target interaction

Abstract

Oligonucleotides are small non-coding regulatory RNA or DNA sequences that bind to specific mRNA locations to impart gene regulation. Identification of oligonucleotides from other small non-coding RNA sequences such as miRNAs, piRNAs etc. is still challenging as oligos exhibit a notable overlap in sequence length and properties with these RNA categories. This work focuses on a probabilistic oligonucleotide classification method based on its distinct underlying feature vectors to identify oligos from other regulatory classes. We propose a computational approach developed using a probabilistic neural network (PNN) based on oligo: target binding characteristics. The performance measure showed promising results when compared with other existing computational methods. Role and contribution of extracted features was estimated using the receiver operating curves. Our study suggests the potentiality of probabilistic approaches over non-probabilistic techniques in oligonucleotide classification problems.