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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.
A sequence-based negative sampling and machine learning framework that learns from protein-protein interactions (PPIs) of different viruses to predict for a novel one, exploiting the shared host proteins. We tested DeNovo on PPIs from different domains to assess generalization. By solving the challenge of generating less noisy negative interactions, DeNovo achieved accuracy up to 81 and 86% when predicting PPIs of viral proteins that have no and distant sequence similarity to the ones used for training, receptively. This result is comparable to the best achieved in single virus-host and intra-species PPI prediction cases. Thus, we can now predict PPIs for virtually any virus infecting human. DeNovo generalizes well; it achieved near optimal accuracy when tested on bacteria-human interactions.
InterSPPI / Inter-Species Protein–Protein Interaction predictor
Allows prediction of plant–pathogen protein–protein interaction. InterSPPI is a webserver, implementing a Random Forest (RF)-based method, that was developed to predict Arabidopsis–pathogen protein-protein interactions (PPIs). The software was tested in cross-species prediction and proteome-wide plant–pathogen PPI identification. It is suitable for predicting plant–pathogen PPIs across various pathogen species.
An open access web oriented program. NACE implements method of evaluating the efficiency of biological processes based on the analysis of the distribution of protein subcellular localization and allows user to evaluate the inter-compartmental efficiency of molecular genetic networks. NACE can be useful in the study of the effectiveness of functioning of various molecular genetic networks, including metabolic, regulatory, host-pathogen interactions and others taking into account tissue-specific gene expression.
bsl_mtl / Bilinear Sparse Low-rank Multitask Learning
Models interactions between human proteins and three different, but related viruses: Hepatitis C, Ebola virus and Influenza A. bsl_mtl uses a shared low-rank structure in addition to a task-specific sparse structure to incorporate the various interactions. This multitask matrix completion based model uses a shared low-rank structure in addition to a task-specific sparse structure to incorporate the various interactions. The parameters of the tool can be interpreted to reveal both general and specific interaction-relevant characteristics of the viruses.
Provides structural controllability with consideration of node connection strength in biological networks. WDNfinder is composed of two algorithms: (1) maximum weight MDS (MWMDS) identification, (2) MWMDS sampling and node classification. It uses its algorithms to find optimal prices for dual problem of maximum weight MCM (MWMCM) linear programming by adding and assigning the dummy edges with small weights. This tool can be used for the human cancer signaling network and p53-mediate DNA damage response network.
PREMER / Paralell Reverse Engineering with Mutual information & Entropy Reduction
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.
KATZHMDA / KATZ measure for Human Microbe–Disease Association
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.
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