Open Access

ARTICLE

Deep-piRNA: Bi-Layered Prediction Model for PIWI-Interacting RNA Using Discriminative Features

by Salman Khan¹, Mukhtaj Khan^1,2, Nadeem Iqbal¹, Mohd Amiruddin Abd Rahman^3,*, Muhammad Khalis Abdul Karim³

1 Department of Computer Science, Abdul Wali Khan University Mardan, Khyber Pakhtunkhwa, 23200, Pakistan
2 Department of Information Technology, University of Haripur, Khyber Pakhtunkhwa, 22620, Pakistan
3 Faculty of Science, Universiti Putra Malaysia, UPM Serdang, 43400, Malaysia

* Corresponding Author: Mohd Amiruddin Abd Rahman. Email:

Computers, Materials & Continua 2022, 72(2), 2243-2258. https://doi.org/10.32604/cmc.2022.022901

Received 22 August 2021; Accepted 11 November 2021; Issue published 29 March 2022

Abstract

Piwi-interacting Ribonucleic acids (piRNAs) molecule is a well-known subclass of small non-coding RNA molecules that are mainly responsible for maintaining genome integrity, regulating gene expression, and germline stem cell maintenance by suppressing transposon elements. The piRNAs molecule can be used for the diagnosis of multiple tumor types and drug development. Due to the vital roles of the piRNA in computational biology, the identification of piRNAs has become an important area of research in computational biology. This paper proposes a two-layer predictor to improve the prediction of piRNAs and their function using deep learning methods. The proposed model applies various feature extraction methods to consider both structure information and physicochemical properties of the biological sequences during the feature extraction process. The outcome of the proposed model is extensively evaluated using the k-fold cross-validation method. The evaluation result shows that the proposed predictor performed better than the existing models with accuracy improvement of 7.59% and 2.81% at layer I and layer II respectively. It is anticipated that the proposed model could be a beneficial tool for cancer diagnosis and precision medicine.

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