中圖分類號：TP391 文獻標志碼：A DOI: 10.16157/j.issn.0258-7998.244946
中文引用格式： 趙歡歡，李顏娥，武斌，等. 基于拓撲結構的度量學習與拓撲傳播的miRNA-疾病關聯預測算法[J]. 電子技術應用，2024，50(9)：67-72.
英文引用格式： Zhao Huanhuan，Li Yan′e，Wu Bin，et al. Topology-based metric learning and topology propagation algorithm for miRNA-disease association prediction[J]. Application of Electronic Technique，2024，50(9)：67-72.

Topology-based metric learning and topology propagation algorithm for miRNA-disease association prediction

Abstract： Mutations and abnormal expressions of miRNA can potentially lead to various diseases. Hence, predicting the latent correlation between miRNA and diseases holds significant importance for the advancement of clinical medicine and drug research. The topology structure constitutes a crucial component of miRNA-disease prediction algorithms. However, the current algorithms inadequately leverage the topological structure, resulting in suboptimal predictive outcomes. Simultaneously, effectively integrating multi-source data is a current research trend. In response to the aforementioned issues, this paper proposes an adaptive algorithm for fusing heterogeneous node structure information (MMTP). MMTP enhances miRNA-disease prediction accuracy by adaptively integrating heterogeneous node structure information through the utilization of first-order neighbors and metapath-induced network learning of structural features, employing metric learning and topology propagation. Results from a 5-fold cross-validation experiment demonstrate that MMTP achieves Area Under the Curve (AUC) of receiver operating characteristic values of 94.81 on the HMDD v3.2 datasets, surpassing other models. Moreover, in a case study focused on renal cancer, all of the top 30 miRNAs predicted by the model are confirmed. The aforementioned research confirms the efficacy of the proposed MMTP model in predicting miRNA-disease correlations.

Key words : deep learning；miRNA-disease association；metric learning；topology structure