# Block sparse Bayes-based fuzzy system for RNA N6-methyladenosine sites prediction

## TL;DR

This paper introduces a new computational model for predicting RNA m6A modification sites with high accuracy and robustness across species and tissues.

## Contribution

The novel BSBL-TSK-FS model uses a Bayesian fuzzy system to improve m6A site prediction accuracy and generalizability.

## Key findings

- BSBL-TSK-FS achieves 0.84–0.95 precision in predicting m6A sites across mouse, human, and rat tissues.
- The model outperforms existing methods by 9.4% in accuracy and shows strong cross-species robustness.
- It achieves an average AUC of 0.9619 and precision of 0.9028 across 11 benchmark datasets.

## Abstract

N6-methyladenosine (m6A) can significantly affect RNA expression, gene regulation, and determination of cell fate. As a common and abundant post-transcriptional modification (PTM) of RNA, m6A is also closely associated with the occurrence of numerous diseases. Thus, identifying the m6A modification site in the RNA sequence is a prerequisite for related research. High-throughput sequencing technology has high requirements and low cost performance. Computational methods have made encouraging progress in site prediction. However, most models only consider the effects of different species, ignoring the simultaneous exploration of RNA modifications in different tissues within the same species. We develop and validate a fuzzy system based on Block Sparse Bayesian Learning (BSBL), named BSBL-TSK-FS, which is a powerful sequence-level m6A prediction model. We introduce a Bayesian method that provides a posterior probability output to produce more sparse solutions so that the model has higher accuracy. The model classifies the m6A sites in several tissues of mouse, human, and rat. Under the five-fold cross-validation method (5-CV), the precision of the BSBL-TSK-FS model is 0.84∼0.95. The accuracy of our model improves by 9.4% over the existing SOTA predictors. BSBL-TSK-FS achieves superior performance over current SOTA methods. Finally, in order to verify the generalizability of the model, we carry out cross-species tests, and the results prove the robustness and adaptability of the model. An accurate and reliable sequence modification prediction model is developed to better understand the complex landscape of methylation modification.

RNA molecules undergo a large number of PTMs that can affect their structure and interaction properties. As the most common type of PTM, N6-methyladenosine (m6A) plays a crucial role in life processes such as gene silencing, cell localization, parental imprinting, and various diseases. Therefore, accurate identification of m6A modification sites from mRNA sequences is of great significance for basic research and drug development. The applicability of experimental methods in large-scale studies is poor. In response to these limitations, computational models have been developed to quickly and economically identify m6A modification sites. In this study, we propose a fuzzy system prediction model, called BSBL-TSK-FS, to identify m6A. We verify the performance of the model on a baseline datasets. Our model, BSBL-TSK-FS, performs well in 11 datasets, with an average AUC value of 0.9619 and an average precision value of 0.9028.