A Novel Human Microbe-Disease Association Prediction Method Based on the Bidirectional Weighted Network

• Published in: Frontiers in microbiology Vol. 10; p. 676
• Main Authors: Li, Hao, Wang, Yuqi, Jiang, Jingwu, Zhao, Haochen, Feng, Xiang, Zhao, Bihai, Wang, Lei
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Media S.A 09.04.2019
• Subjects: association prediction; bidirectional recommendations; bidirectional weighted network; disease; microbe; Microbiology; association prediction; bidirectional weighted network; disease; microbe; bidirectional recommendations
• ISSN: 1664-302X; 1664-302X
• DOI: 10.3389/fmicb.2019.00676

The survival of human beings is inseparable from microbes. More and more studies have proved that microbes can affect human physiological processes in various aspects and are closely related to some human diseases. In this paper, based on known microbe-disease associations, a bidirectional weighted network was constructed by integrating the schemes of normalized Gaussian interactions and bidirectional recommendations firstly. And then, based on the newly constructed bidirectional network, a computational model called BWNMHMDA was developed to predict potential relationships between microbes and diseases. Finally, in order to evaluate the superiority of the new prediction model BWNMHMDA, the framework of LOOCV and 5-fold cross validation were implemented, and simulation results indicated that BWNMHMDA could achieve reliable AUCs of 0.9127 and 0.8967 ± 0.0027 in these two different frameworks respectively, which is outperformed some state-of-the-art methods. Moreover, case studies of asthma, colorectal carcinoma, and chronic obstructive pulmonary disease were implemented to further estimate the performance of BWNMHMDA. Experimental results showed that there are 10, 9, and 8 out of the top 10 predicted microbes having been confirmed by related literature in these three kinds of case studies separately, which also demonstrated that our new model BWNMHMDA could achieve satisfying prediction performance.