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.
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.
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.
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 algorithm that can be used to identify common sets of host-pathogen interactions by aligning (mapping) host-to-host and pathogen-to-pathogen proteins from two interaction datasets. HPIA algorithm were used to compare human-B. mallei interactions to those of human-Y. pestis and human-S. enterica. HPIA identified a statistically significant number of aligned interactions.
Provides a computational model for deducing the most potential human microbe-disease association. LRLSHMDA introduces Gaussian interaction profile kernel similarity and Laplacian regularized least squares (LapRLS) classification to exploit the implicative information of vertices and edges in known microbe-disease association network topology structures.
Predicts human microbe-disease associations. NCPHMDA is a non-parametric and global network-based model, based on network consistency projection, that integrates Gaussian interaction profile kernel similarity of microbes and diseases, and symptom-based disease similarity. The software method is applicable in situations where there are very few verified microbe-disease associations.
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).
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.
Serves for predicting potential microbe-noninfectious disease associations (MDAs) on a large scale by only using a set of approved microbe-disease associations. BMCMDA is a program that can be used for two types of problem in bioinformatics: the inference of the binary relationship (1) between mono-partite objects (protein-protein interaction, drug-drug interaction or drug combination), and (2) between bi-partite objects (drug-target interaction, gene-disease association, or RNA-disease association).
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.
Predicts potential microbe-disease associations using a graph-regularized non-negative matrix factorization model. NMFMDA is based on known microbe-disease associations, using a Gaussian interaction profile kernel similarity measure to perform. This program is designed to compute microbial similarity and disease similarity, and to apply a logistic function to regulate disease similarity.
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.
Scores the functional impact of mutations regarding to Salmonella enterica. invasive_salmonella allows detection of patterns of gene degradation associated with invasiveness in Salmonella. It is composed of three directories: (1) a directory for analyzing Salmonella strains; (2) a directory offering code for original analysis; and (3) a directory permitting users to build their own model to detect genes associated with a phenotype.
Investigates relationships between human diseases and microbial communities. PRWHMDA supplies a computational model constructed from information recorded into the Human Microbe-Disease Association Database (HMDAD). It intends to detect disease-related microbes and to assist users in understanding the relationship between microorganisms and human organism. However, its analysis can be performed only with microbes and diseases censused into HMDAD.
Allows users to predict human microbe-disease association. CMFHMDA aims to update the correlation matrix about microbes and diseases for inferring disease-related microbes. Moreover, this program can be used for detecting potential microbes associated with important non-infectious human diseases.
Offers an integration of deep learning for complex image analysis. HRMAn is a software for the analysis of host-pathogen interaction at the single-cell level. This pipeline is designed to work with all file types acquired on any high content imaging (HCI) platform or fluorescence microscope. This method is based on the data handling environment Eclipse-KNIM.
Evaluates the performance of method developed for the simulation of the evolution of a plant pathogen in a cropping landscape. Landsepi can serve to assess the combination of different sources of resistance. It allows the exploration of different spatio-temporal deployment strategies. This tool gives a description of the epidemiological and the evolutionary outcomes of a deployment strategy.
Allows users to reproduce interactions between humans and mosquito agents on a defined area. SoNA3BS is composed of two parts allowing simulation and data analysis and includes four mosquitoes’ development stages. It intends to investigate spatial arrangement influence on vector-borne disease transmission and to reconstruct vectorial-contact networks. It has been created to help in understanding dengue epidemic processes.