Table_1_A Novel Human Microbe-Disease Association Prediction Method Based on the Bidirectional Weighted Network.xls

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.

人类的生存与微生物密不可分。越来越多的研究表明，微生物能够从多个方面影响人类的生理过程，并且与某些人类疾病密切相关。在本文中，基于已知的微生物与疾病关联，首先通过整合归一化高斯交互和双向推荐方案，构建了一个双向加权网络。随后，基于所构建的双向网络，开发了一种名为BWNMHMDA的计算模型，用以预测微生物与疾病之间潜在的关系。最终，为了评估新预测模型BWNMHMDA的优越性，实施了LOOCV和5折交叉验证的框架，模拟结果显示，BWNMHMDA在这两种不同的框架中分别实现了可靠的AUC值0.9127和0.8967 ± 0.0027，超越了某些最先进的方法。此外，通过对哮喘、结直肠癌和慢性阻塞性肺病的案例研究，进一步评估了BWNMHMDA的性能。实验结果表明，在这三种案例研究中，前10个预测微生物中分别有10、9和8个已被相关文献所证实，这也证明了我们新的模型BWNMHMDA能够达到令人满意的预测性能。