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作者机构:Dalian Maritime Univ Dept Informat Sci & Technol Dalian 116026 Peoples R China DUT Artificial Intelligence Inst Dalian 116024 Peoples R China Dalian Key Lab Artificial Intelligence Dalian 116024 Peoples R China Jiangnan Univ Sch Artificial Intelligence & Comp Sci Wuxi 214122 Peoples R China Jilin Univ Minist Educ Key Lab Symbol Computat & Knowledge Engn Changchun 130012 Peoples R China Northeastern Univ Sch Comp Sci & Engn Shenyang 110819 Peoples R China
出 版 物:《IEEE TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS》
年 卷 期:2025年第22卷第1期
页 面:203-215页
核心收录:
基 金:National Natural Science Foundation of China Innovation Support Program for Dalian High Level Talents [2023RQ007] Dalian Excellent Young Project [2022RY35]
主 题:Diseases Predictive models Autoencoders Feature extraction Biological system modeling Bioinformatics Kernel Computational biology Heterogeneous networks Accuracy Potential miRNA-disease association similarity-association-similarity metapaths heterogeneous-hyper network graph convolutional networks
摘 要:In recent years, microRNA (miRNA) has been recognized as crucial in the progression of human diseases. However, existing computational methods for identifying miRNA-disease associations often overlook the rich association information contained in specific long-distance pathways and lack effective exploration of potential associations. In this study, we propose a biologically interpretable similarity-association-similarity metapath and heterogeneous-hyper network (HeteroHyperNet) learning approach for miRNA-disease association prediction (MHMDA). In MHMDA, a similarity-association-similarity multi-hop metapaths learning method based on hierarchical attention perception is proposed to explore specific long-distance associated pathway information connecting potentially associated miRNAs and diseases. In addition, a HeteroHyperNet learning approach integrating heterogeneous network and hyper network is designed to progressively learn direct association information and potential association information between miRNA and disease. The similarity-association-similarity metapath with hierarchical attention significantly enhances the learning of long-distance biological associations, while the HeteroHyperNet comprehensively learns the known and potential associations of miRNA-disease, greatly improving the richness and accuracy of information. A large number of experimental results show that MHMDA has demonstrated excellent performance in the prediction of miRNA-disease association. In addition, cross independent dataset experiment and cold start experiment on miRNA and disease prove the effectiveness of MHMDA on sparse association points, and its stability and reliability in predicting potential miRNA-disease association are further confirmed.