Role of Optimization in RNA–Protein-Binding Prediction

TL;DR

This paper explores how different optimization methods affect the accuracy of predicting RNA–protein binding using deep learning models.

Contribution

The study compares grid search, random search, and Bayesian optimization for hyperparameter tuning in RNA–protein binding prediction.

Findings

Bayesian optimization achieved the highest AUC of 94.42% on the ELAVL1C dataset.

The mean AUC across 24 datasets was 85.30%, showing the effectiveness of optimization methods.

Optimization significantly impacts the performance of RNA–protein binding prediction models.

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.

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