An Intelligent Model for Predictions of PIWI-Interacting RNAs and Their Functions

Abstract

A documented class of short non-coding RNA molecules is known as PIWI interacting RNA (PiRNA). The creation of new drugs and the identification of various tumor types are linked to the PiRNA molecules. Additionally, it is related to controlling transcription of genes, squelching transposons, and preserving genomic stability. The discovery of piRNAs and their functionality has grown to be a significant research topic in bioinformatics because of the crucial influence that piRNAs play in biological processes. The 2L-piRNA-ML predictor is a strong two-layer predictor that is suggested in this research to enhance the prediction of PiRNA and their functionality. The suggested model uses Quadratic Discriminant Analysis Classifier, Linear Discriminant Analysis, Passive Aggressive Classifier, Extra Tree Classifier, Logistic Regression, Random Forest, Ridge Classifier CV for classification. It also employs DNC and TNC for extraction of features. The suggested model is created using a two-layer construction strategy. The 1st layer makes a prediction about a given sequence whether it is PiRNA or not, and the 2nd layer makes a prediction about a given PiRNA sequence whether it is having the function of instructing target mRNA deadenylation or not. Proposed model achieved 95.65 % accuracy at the first layer and 92.30% accuracy at the second layer.