GCSENet: A GCN, CNN and SENet ensemble model for microRNA-disease association prediction

Zhong Li, Kaiyancheng Jiang, Shengwei Qin, Yijun Zhong, Arne Elofsson

Abstract: Author summary Identifying miRNA-disease associations accelerates the understanding towards pathogenicity, which is beneficial for the development of treatment tools for diseases. Different from existing methods, our GCSENet captures the deep relationship between miRNA and disease through three heterogeneous graphs (disease, gene and miRNA) to promote an accurate prediction result. We performed the 10-fold cross validation to evaluate the performance of GCSENet, which can outperform many classic methods. Furthermore, we carried out case studies on four important diseases, which were used to evaluate the performance of our model regarding to the associations with experimental evidences in literature. The result shows that most predicted miRNAs (48 for lung neoplasms, 48 for heart failure, 48 for breast cancer and 50 for glioblastoma) in the top 50 predictions were confirmed in HMDD v3.0. As a result, it shows that GCSENet can make reliable predictions and guide experiments to uncover more miRNA-disease associations.