MultiStage Probabilistic Bipartite Graph Algorithm  Effect of Herbal Medicines on the Gut Ecosystem The wide variety of bacterial population in the human gastrointestinal tract (the gut) are the key players in maintaining host physiological functions and colon health. Also, these gut microbiotamediated productions of metabolites, while mostly beneficial to the host, have also been shown to contribute to the imbalance of the protective versus harmful intestinal bacterial species (dysbiosis). Such imbalance could result in intestinal and extraintestinal disorders, including inflammatory bowel disease (IBD), diabetes mellitus (DM), obesity, etc. Furthermore, herbal medicines are capable of modulating the bacterial metabolic pathways by affecting the bacterial gene functions that serving as drug targets. This holds a great promise in balancing the gut ecosystem and prevent microbialassociated diseases. However, the diversity of small molecules in the herbs and the complexity of their mechanism of action poses a great challenge for data integration and herbal drugtarget association identification. To overcome this challenge and get invaluable insights into microbiomeassociated disease biology, a systemslevel characterization of the effects of the small molecules from herbs on the bacterial genes and metabolic pathways were made in the current study. We proposed a computational networkbased approach; the multistage probabilistic bipartite graph algorithm (MPBA), which integrates the complex heterogeneous metabolismpathway data from herbs and gut bacteria. This approach provided a graphically intuitive solution to study the impact of herbs on gut microorganisms and employed a probabilistic latent semantic analysis (PLSA) framework to allow estimation of candidate entities. A case study using the 8 most important pathways during IBD and 405 enzymes belonging to these pathways was conducted. Our results show that 273 pathway enzymes were harbored by the bacteria, and 262 of them were targeted by small molecules present in 24 culinary herbs. The proposed MPBA enabled simplification of complex biological models needed for an indepth study of the herbmicrobes complex system.

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.