A Method for Predicting Disease-Related MicroRNAs Based on Topological Information
GAO Peng1, CHEN Zhi-hua2
1. School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China;
2. Institute of Computing Science and Technology, Guangzhou University, Guangzhou, Guangdong 510006, China
Abstract：Studies show that mutations or abnormalities in micRNAs can lead to many diseases, and the identification of disease-associated microRNAs (miRNAs) can help diagnose and treat related diseases.However, it is costly and long-term to obtain accurate correlations through biological experiments.Therefore, this paper proposes a machine learning method (HNDLM) that uses network topology information to predict disease-miRNA associations.HNDLM avoids the construction of similarity networks, but applies the network embedding method proposed in recent years to biological networks.Experimental results show that HNDLM performs better than MIDPE, MIDP, WBSMDA, RLSMDA, CPTL, HDMP classical algorithms in accuracy and AUC value.In case study, the top 30 candidate miRNAs recommended by HNDLM can be confirmed by previous experiments.HNDLM can discover the potential disease-miRNA relationship and help to further study the pathogenesis of the disease, promote the development of bioinformatics.
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2020, Vol. 48 
