THGC_MDA: a method for predicting the associations between m¹A modification and diseases based on ternary...

m¹A modification, as a pivotal RNA epigenetic modification, plays a central regulatory role in the pathogenesis and progression of complex human diseases, including cancer. Exploring the potential associations between m¹As and diseases are an important approach to revealing the molecular mechanism of disease onset. However, traditional biological experiments have the limitations of time-consuming and labor-intensive, resulting in an extremely scarce amount of verified m¹A-disease association data. Meanwhile, the existing computational prediction methods are mostly limited to specific application scenarios and rely solely on the direct correlation data between m¹As and diseases. They do not fully integrate multi-dimensional biological information and thus are unable to achieve efficient and accurate association predictions. In view of this, this study proposes a method for predicting the association between m¹A modification and diseases based on a ternary heterogeneous network and GCN. By introducing circRNA as an intermediate connection node, a ternary association network of m¹A-circRNA-disease is constructed, which effectively enriches the dimension of feature information for both m¹A and diseases. Meanwhile, leveraging the feature learning capability of Graph Convolutional Network, the extraction and representation of their features are realized. The experimental results demonstrate that the proposed approaches significantly outperforms existing mainstream methods in predictive performance, substantially enhancing the accuracy and reliability of m¹A-disease association prediction. Furthermore, case validation has further confirmed that the predicted candidate m¹A sites participate in regulating disease-related gene expression networks by modulating core processes such as RNA localization, stability, and translation efficiency, thereby providing novel insights into the investigation of disease pathogenesis.