SACMDA: MiRNA-Disease Association Prediction with Short Acyclic Connections in Heterogeneous Graph
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MiRNA-disease association is important to disease diagnosis and treatment. Prediction of miRNA-disease associations is receiving increasing attention. Using the huge number of known databases to predict potential associations between miRNAs and diseases is an important topic in the field of biology and medicine. In this paper, we propose a novel computational method of with Short Acyclic Connections in Heterogeneous Graph (SACMDA). SACMDA obtains AUCs of 0.8770 and 0.8368 during global and local leave-one-out cross validation, respectively. Furthermore, SACMDA has been applied to three important human cancers for performance evaluation. As a result, 92% (Colon Neoplasms), 96% (Carcinoma Hepatocellular) and 94% (Esophageal Neoplasms) of top 50 predicted miRNAs are confirmed by recent experimental reports. What’s more, SACMDA could be effectively applied to new diseases and new miRNAs without any known associations, which overcomes the limitations of many previous methods.
KeywordsSACMDA MiRNA-disease association Computational model
This work is supported by National Nature Science Foundation of China (61701149, 61525206, 61671196, 61327902), Zhejiang Province Nature Science Foundation of China LR17F030006.
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