Prediction of MicrobeDrug Associations Based on KATZ Measure Complex and diverse microbial communities do not only take important roles in human health and disease, but also are clinically drug targets. Predicting potential microbedrug associations is helpful to understand complex mechanisms of microbes in clinical treatment, drug discovery, combinations, and repositioning. But potential microbedrug associationu0027s prediction is timeconsuming and expensive by using biological experiments, while computational methods can effectively overcome these limitations. To predict Human MicrobeDrug Association, a new computational method of KATZ measure (HMDAKATZ) is proposed. As we have known so far, HMDAKATZ is the first tool to predict potential associations between microbe and drug. In our method, we firstly construct the microbe similarity network by computing the GIP kernel similarity of microbes based on known microbedrug associations. Then, the drug similarity network is constructed by integrating the chemical structures similarity and GIP kernel similarity of drugs. We further construct the microbedrug heterogeneous network based on two similarity networks and known microbedrug associations. Based on the microbedrug heterogeneous network, we apply HMDAKATZ to predict potential microbedrug associations based on the microbedrug heterogeneous network. The experimental result shows that HMDAKATZ has obtained an average area under the curve (AUC) value of  0.9010pm 0.0020  in the 5fold cross validation (5fold CV). Furthermore, a case study also demonstrate that 100% of top 20 potential drugs of human immunodeficiency virus have been validated by existing literature, confirming the effectiveness of HMDAKATZ.

Predicting DrugDisease Treatment Associations Based on Topological Similarity and Singular Value Decomposition To assist drug development, many computational methods have been proposed to identify potential drugdisease treatment associations before wet experiments. Based on the assumption that similar drugs may treat similar diseases, most methods need the similarities of drugs and diseases, and they will not work if the biological or chemical features for computing similarities are missing. Besides, being lack of validated negative samples in the drugdisease associations data, most methods simply select some unlabeled samples as negative ones, which may introduce noises. Herein, we propose a new method (TSSVD) which only uses those known drugprotein, diseaseprotein and drugdisease interactions to predict the potential drugdisease associations. In a constructed drugproteindisease heterogeneous network, we consider the common neighbors of drugs and diseases to obtain the topological similarity. Then the topological similarity matrix of drugs (diseases) will be used to get the low dimensional embedding representations of drugdisease pairs. Finally, a Random Forest classifier is trained to do the prediction. To train a more reasonable model, we select out some reliable negative samples based on the kstep neighbors relationships between drugs and diseases. Compared with some stateoftheart methods, we use less information but achieve better or comparable performance. Meanwhile, our strategy for selecting reliable negative samples can improve the performances of these methods.