Deep-m5U: a deep learning-based approach for RNA 5-methyluridine modification prediction using optimized feature integration.

Journal: BMC bioinformatics
Published Date: Nov 19, 2024

Abstract

BACKGROUND: RNA 5-methyluridine (m5U) modifications play a crucial role in biological processes, making their accurate identification a key focus in computational biology. This paper introduces Deep-m5U, a robust predictor designed to enhance the prediction of m5U modifications. The proposed method, named Deep-m5U, utilizes a hybrid pseudo-K-tuple nucleotide composition (PseKNC) for sequence formulation, a Shapley Additive exPlanations (SHAP) algorithm for discriminant feature selection, and a deep neural network (DNN) as the classifier.

Authors

  • Sumaiya Noor
    Business and Management Sciences Department, Purdue University, West Lafayette, IN, USA.
  • Afshan Naseem
    Institute of Oceanography and Environment (INOS), Universiti Malaysia Terengganu, 21030, Kuala Nerus, Terengganu, Malaysia.
  • Hamid Hussain Awan
    Department of Computer Science, Muslim Youth University, Islamabad, Pakistan.
  • Wasiq Aslam
    Department of Computer Science, Muslim Youth University, Islamabad, Pakistan.
  • Salman Khan
  • Salman A AlQahtani
    Research Chair of Pervasive and Mobile Computing, Department of Computer Engineering, College of Computer and Information Sciences, King Saud University, Riyadh 11574, Saudi Arabia.
  • Nijad Ahmad
    Department of Computer Science, Khurasan University Jalalabad, Jalalabad, Afghanistan. Nijad@khurasan.edu.af.

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