Can we predict protein-protein interactions (PPIs) of a novel virus with its host? Three major problems arise: the lack of known PPIs for that virus to learn from, the cost of learning about its proteins and the sequence dissimilarity among viral families that makes most methods inapplicable or inefficient.
Allows exploration of the Plant-Pathogen Immune Network. PPIN includes more than 3000 interactions among about 900 immune related proteins. Of these 900 immune related proteins we count over 80 combined effectors from the bacterium Pseudomonas syringae.
Allows users to recover and determine the strength and causality of interactions. PREMER allows incorporation of prior knowledge, imputation of missing data, and correction of outliers. This tool is a network inference toolbox, able to generate inferred network with plots of the mutual information between variables for different possible time delays. PREMER uses several information-theoretic measures which are estimated from data using adaptive partitioning algorithm.
A computational framework for inferring pathway-based interactions between a host and a pathogen that relies on the idea of metabolite hijacking. Hi-Jack searches metabolic network data from hosts and pathogens, and identifies candidate reactions where hijacking occurs. A novel scoring function ranks candidate hijacked reactions and identifies pathways in the host that interact with pathways in the pathogen, as well as the associated frequent hijacked metabolites.
Predicts potential microbe-disease associations. KATZHMDA is a computational model that can predict new microbe-disease associations in a large scale by combining the known microbe-disease associations and Gaussian interaction profile kernel similarity for microbes and diseases. The software can be used for obtaining more novel microbes associated with important noninfectious human diseases and is can therefore be useful for drug discovery and human medical improvement.
Consists of a computational model of path-based human microbe-disease association prediction. PBHMDA is a program based on a heterogeneous network composed of the known microbe-disease associations and Gaussian interaction profile kernel similarity for microbes and diseases. This method can be applied to infer potential microbe-disease associations. Moreover, this tool cannot perform for the new microbes without known associated diseases, and new diseases without known associated microbes.
Consists of a predictive tool for potential microbe-disease associations. BiRWHMDA is a computational method that executes a bi-random walk algorithm on the heterogeneous network to perform. The software was developed for determining human microbe-disease associations. Its performance was assessed by implementing case studies for asthma and inflammatory bowel disease (IBD).
A software tool that allows users to easily create an integrated human protein network, or HIV-host networks. A major advantage of this platform compared to other visualization tools is its web-based format, which requires no software installation or data downloads. GPS-Prot allows novice users to quickly generate networks that combine both genetic and protein-protein interactions between HIV and its human host into a single representation. Ultimately, the platform is extendable to other host-pathogen systems.