Abstract
Piwi interacting RNA (piRNA) is a recognized group of small non-coding RNA molecules. The piRNA molecules are associated with multiple tumors type diagnosis and drug development. It is also linked to regulate gene expression, suppressing transposon and maintains genome integrity. Due to a vital role of piRNAs in biology, the identification of piRNAs and their function has become an important area of research in computational biology. This paper proposes a robust two-layer predictor called "piRNA (2L)-PseKNC" to improve prediction of piRNAs and their function. The proposed predictor employing hybrid pseudo-K-tuple nucleotide composition (PseKNC) for sequence formulation, unsupervised principal component analysis (PCA) algorithm for discriminant feature selection and deep neural network (DNN) as a classifier. The proposed predictor was designed based on two layers approach. The first layer predicts either the encoded sequence belongs to piRNA or non-piRNA sequence and the second layer predict the selected piRNA sequence is functional piRNA or non-functional piRNA sequence. The overall accuracies of the proposed model using 5-fold cross validation test were 94.73% at first layer and 85.21% at second layer, surpassing the existing predictors with accuracies improvement 7.59% and 2.81% at the first layer and at the second layer respectively.