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ORIGINAL RESEARCH article

Front. Genet.
Sec. Computational Genomics
Volume 15 - 2024 | doi: 10.3389/fgene.2024.1349546

DeepSplice: A Deep Learning Approach for Accurate Prediction of Alternative Splicing Events in the Human Genome

Provisionally accepted
Mohammad Abrar Mohammad Abrar 1Didar Hussain Didar Hussain 2*Izaz A. Khan Izaz A. Khan 2Fasee Ullah Fasee Ullah 3*Mohd Anul Haq Mohd Anul Haq 4Mohammed A. Aleisa Mohammed A. Aleisa 4Abdullah Alenizi Abdullah Alenizi 4Shashi Bhushan Shashi Bhushan 5Sheshikala Martha Sheshikala Martha 6
  • 1 Other, Muscat, Oman
  • 2 Bacha Khan University Charsadda, Charsadda, Khyber Pakhtunkhwa, Pakistan
  • 3 University of Technology Petronas, Tronoh, Malaysia
  • 4 Department of Computer Science, College of Computer Sciences and Information Technology, Majmaah University, Al'Majmaah, Saudi Arabia
  • 5 School of Computer Science and Artificial Intelligence, SR University, Warangal, India
  • 6 Department of Computer and Information Sciences, Faculty of Science and Information Technology, University of Technology Petronas, Seri Iskandar, Perak Darul Ridzuan, Malaysia

The final, formatted version of the article will be published soon.

    Alternative splicing (AS) is a crucial process in genetic information processing that generates multiple mRNA molecules from a single gene, producing diverse proteins. Accurate prediction of AS events is essential for understanding various physiological aspects, including disease progression and prognosis. Machine learning (ML) techniques have been widely employed in bioinformatics to address this challenge. However, existing models have limitations in capturing AS events in the presence of mutations and achieving high prediction performance. To overcome these limitations, this research presents deep splicing code (DSC), a deep learning (DL)-based model for AS prediction. The proposed model aims to improve predictive ability by investigating state-of-the-art techniques in AS and developing a DL model specifically designed to predict AS events accurately. The performance of the DSC model is evaluated against existing techniques, revealing its potential to enhance the understanding and predictive power of DL algorithms in AS. It outperforms other models by achieving an average AUC score of 92%. The significance of this research lies in its contribution to identifying functional implications and potential therapeutic targets associated with AS, with applications in genomics, bioinformatics, and biomedical research.The findings of this study have the potential to advance the field and pave the way for more precise and reliable predictions of AS events, ultimately leading to a deeper understanding of genetic information processing and its impact on human physiology and disease.

    Keywords: Alternative Splicing, machine learning, deep learning, CNN, neural networks

    Received: 20 Dec 2023; Accepted: 21 May 2024.

    Copyright: © 2024 Abrar, Hussain, Khan, Ullah, Haq, A. Aleisa, Alenizi, Bhushan and Martha. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence:
    Didar Hussain, Bacha Khan University Charsadda, Charsadda, Khyber Pakhtunkhwa, Pakistan
    Fasee Ullah, University of Technology Petronas, Tronoh, Malaysia

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.