Role of Optimization in RNA-Protein-Binding Prediction

Shrooq Alsenan; Isra Al-Turaiki; Mashael Aldayel; Mohamed Tounsi

doi:10.3390/cimb46020087

Role of Optimization in RNA-Protein-Binding Prediction

Curr Issues Mol Biol. 2024 Feb 4;46(2):1360-1373. doi: 10.3390/cimb46020087.

Authors

Shrooq Alsenan¹, Isra Al-Turaiki², Mashael Aldayel³, Mohamed Tounsi⁴

Affiliations

¹ Information Systems Department, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia.
² Department of Computer Science, College of Computer and Information Sciences, King Saud University, Riyadh 11653, Saudi Arabia.
³ Information Technology Department, College of Computer and Information Sciences, King Saud University, Riyadh 11451, Saudi Arabia.
⁴ Department of Computer Science, College of Computer and information Sciences, Prince Sultan University, P.O. Box 66833, Riyadh 12435, Saudi Arabia.

PMID: 38392205
DOI: 10.3390/cimb46020087

Abstract

RNA-binding proteins (RBPs) play an important role in regulating biological processes, such as gene regulation. Understanding their behaviors, for example, their binding site, can be helpful in understanding RBP-related diseases. Studies have focused on predicting RNA binding by means of machine learning algorithms including deep convolutional neural network models. One of the integral parts of modeling deep learning is achieving optimal hyperparameter tuning and minimizing a loss function using optimization algorithms. In this paper, we investigate the role of optimization in the RBP classification problem using the CLIP-Seq 21 dataset. Three optimization methods are employed on the RNA-protein binding CNN prediction model; namely, grid search, random search, and Bayesian optimizer. The empirical results show an AUC of 94.42%, 93.78%, 93.23% and 92.68% on the ELAVL1C, ELAVL1B, ELAVL1A, and HNRNPC datasets, respectively, and a mean AUC of 85.30 on 24 datasets. This paper's findings provide evidence on the role of optimizers in improving the performance of RNA-protein binding prediction.

Keywords: Bayesian optimizer; RNA-binding proteins; artificial intelligence; bioinformatics; convolutional neural network (CNN); deep learning; grid search; machine learning; optimization; proteins; random search optimizer.