Advances in next-generation sequencing and high-throughput techniques have enabled the generation of vast amounts of diverse omics data. These big data provide an unprecedented opportunity in biology, but impose great challenges in data integration, data mining, and knowledge discovery due to the complexity, heterogeneity, dynamics, uncertainty, and high-dimensionality inherited in the omics data. Network has been widely used to represent relations between entities in biological system, such as protein-protein interaction, gene regulation, and brain connectivity (i.e. network construction) as well as to infer novel relations given a reconstructed network (aka link prediction). Particularly, heterogeneous multi-layered network (HMLN) has proven successful in integrating diverse biological data for the representation of the hierarchy of biological system. The HMLN provides unparalleled opportunities but imposes new computational challenges on establishing causal genotype-phenotype associations and understanding environmental impact on organisms. In this review, we focus on the recent advances in developing novel computational methods for the inference of novel biological relations from the HMLN. We first discuss the properties of biological HMLN. Then we survey four categories of state-ofthe-art methods (matrix factorization, random walk, knowledge graph, and deep learning). Thirdly, we demonstrate their applications to omics data integration and analysis. Finally, we outline strategies for future directions in the development of new HMLN models.

Background Microbes are associated with many human diseases and influence drug efficacy. Small-molecule drugs may revolutionize biomedicine by fine-tuning the microbiota on the basis of individual patient microbiome signatures. However, emerging endeavors in small-molecule microbiome drug discovery continue to follow a conventional “one-drug-one-target-one-disease” process. A systematic pharmacology approach that would suppress multiple interacting pathogenic species in the microbiome, could offer an attractive alternative solution. Results We construct a disease-centric signed microbe–microbe interaction network using curated microbe metabolite information and their effects on host. We develop a Signed Random Walk with Restart algorithm for the accurate prediction of effect of microbes on human health and diseases. With a survey on the druggable and evolutionary space of microbe proteins, we find that 8–10% of them can be targeted by existing drugs or drug-like chemicals and that 25% of them have homologs to human proteins. We demonstrate that drugs for diabetes can be the lead compounds for development of microbiota-targeted therapeutics. We further show that the potential drug targets that specifically exist in pathogenic microbes are periplasmic and cellular outer membrane proteins. Conclusion The systematic studies of the polypharmacological landscape of the microbiome network may open a new avenue for the small-molecule drug discovery of the microbiome. We believe that the application of systematic method on the polypharmacological investigation could lead to the discovery of novel drug therapies.

21 An increasing body of evidence suggests that microbes are not only strongly associated with many 22 human diseases but also responsible for the efficacy, resistance, and toxicity of drugs. Small-23 molecule drugs which can precisely fine-tune the microbial ecosystem on the basis of individual 24 patients may revolutionize biomedicine. However, emerging endeavors in small-molecule 25 microbiome drug discovery continue to follow a conventional "one-drug-one-target-one-disease" 26 process. It is often insufficient and less successful in tackling complex systematic diseases. A 27 systematic pharmacology approach that intervenes multiple interacting pathogenic species in the 28 microbiome, could offer an attractive alternative solution. Advances in the Human Microbiome 29 Project have provided numerous genomics data to study microbial interactions in the complex 30 microbiome community. Integrating microbiome data with chemical genomics and other 31 biological information enables us to delineate the landscape for the small molecule modulation of 32 the human microbiome network. In this paper, we construct a disease-centric signed microbe-33 microbe interaction network using metabolite information of microbes and curated microbe effects 34 on human health from published work. We develop a Signed Random Walk with Restart algorithm 35 for the accurate prediction of pathogenic and commensal species. With a survey on the druggable 36 and evolutionary space of microbe proteins, we find that 8-10% of them can be targeted by existing 37 drugs or drug-like chemicals and that 25% of them have homologs to human proteins. We also 38 demonstrate that drugs for diabetes are enriched in the potential inhibitors that target pathogenic 39 microbe without affecting the commensal microbe, thus can be repurposed to modulate the 40 microbiome ecosystem. We further show that periplasmic and cellular outer membrane proteins 41 are overrepresented in the potential drug targets set in pathogenic microbe, but not in the 3 42 commensal microbe. The systematic studies of polypharmacological landscape of the microbiome 43 network may open a new avenue for the small-molecule drug discovery of microbiome.