MFF-nDA: A Computational Model for ncRNA–Disease Association Prediction Based on Multimodule Fusion

Noncoding RNAs(ncRNAs), including piwi-interacting RNA(piRNA), long noncoding RNA(lncRNA), microRNA(miRNA), small nucleolar RNA(snoRNA), and circular RNA(circRNA), contribute significantly to gene expression regulation and serve as key factors in disease association studies and health-related exploration. Accurate prediction of ncRNA-disease associations is crucial for elucidating disease mechanisms and advancing therapeutic development. Recently, computational models based on a graph neural network have extensively emerged for identifying associations among various ncRNAs and diseases. However, existing computational models have not fully utilized integrative information on ncRNs and diseases, and reliance on GNN-based models alone may be limited in performance due to oversmoothing issues. On the other hand, existing models are mainly targeted at a specific type of ncRNA and may not be applicable to most ncRNAs. Therefore, to overcome these limitations, we propound a computational model MFF-nDA based on multimodule fusion. Specifically, we first introduce five types of similarity network information, including three types of ncRNA and two types of disease similarity information, in order to fully explore and optimize the multisource feature information on these entities. Subsequently, we establish three modules: heterogeneous network representation module based on Transformer, association network representation module based on graph convolutional network (GCN), and topological structure representation module based on graph attention network (GAT), which capture diverse features of nodes in heterogeneous networks and topological structure information reflected in association networks. The complementary effects of the three modules also help relieve the oversmoothing issue to some extent. By leveraging the multimodule fusion learning to comprehensively capture the diverse features of these entities, our model outperforms the available state-of-the-art methods, achieving an AUC greater than 0.9000 for each dataset. This demonstrates the highest predictive performance, making it a valuable tool for identifying potential ncRNA associated with diseases. The code of MFF-nDA can be accessed at https://github.com/Jack-Cxy/MFF-nDA.