Predicting Disease-Associated N7-Methylguanosine (m7G) Sites via Random Walk on Heterogeneous Network

Abstract

Recent studies revealed that the modification of N7-methylguanosine (m⁷G) has associations with many human diseases. Effectively identifying disease-associated m⁷G methylation sites would provide crucial clues for disease diagnosis and treatment. Previous studies have developed computational methods to predict disease-associated m⁷G sites based on similarities among m⁷G sites and diseases. However, few have focused on the influence of the known m⁷G-disease association information on calculating similarity measures of m⁷G site and disease, which potentially promotes the identification of the disease-associated m⁷G sites. In this work, we propose а computational method called m⁷GDP-RW to predict m⁷G-disease associations by random walk algorithm. m⁷GDP-RW first incorporates the feature information of m⁷G site and disease with the known m⁷G-disease associations to compute m⁷G site similarity and disease similarity. Then m⁷GDP-RW combines the known m⁷G-disease associations with the computed similarity of m⁷G site and disease to construct a m⁷G-disease heterogeneous network. Finally, m⁷GDP-RW utilizes a two-pass random walk with restart algorithm to find novel m⁷G-disease associations on the heterogeneous network. The experimental results show that our method achieves higher prediction accuracy compared to the existing methods. The study case also demonstrates the effectiveness of m⁷GDP-RW in discovering potential m⁷G-disease associations.