DIGEST greatly facilitates in silico validation of gene and disease sets, clusterings or subnetworks via fully automated pipelines comprising disease and gene ID mapping, enrichment analysis, comparisons of shared genes and variants and background distribution estimation. Moreover, functionality is provided to automatically update the external databases used by the pipelines. DIGEST hence allows the user to assess the statistical significance of candidate mechanisms with regard to functional and genetic coherence and enables the computation of empirical P-values with just a few mouse clicks.

GraphSimViz provides a tool to estimate the agreement of different disease ontologies using network medicine. Users can compare diseasome, drugome and drug-disease network neighborhoods for selected drugs or diseases to assess how well or poorly these entities are represented in network medicine.

GrandForest is an online tool for de novo endophenotyping and mechanotyping of diseases using omics data. PathClass is an online tool for breast cancer subtyping from gene expression data. Instead of using gene panels, we employ pathway activity patterns for this purpose, which render statistically way more robust. Our web application allows to predict breast cancer subtypes for new samples, including custom uploaded samples as well as samples found on the Gene Expression Omnibus. Furthermore, selected pathways used for the individual predictors can be investigated.

ROBUST is a Python tool which uses enumeration of diverse prize-collecting Steiner trees to compute disease modules that are robust to random bias.

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ROBUST-Web  is a user-friendly web implementation of the ROBUST disease module mining algorithm, with additional options to improve robustness by making use of study bias data. Taking advantage of the Drugst.One interface, ROBUST-Web supports further exploration of discovered targets via integrated gene set enrichment analysis, tissue expression annotations, and drug- protein, drug-disease, and disease-gene associations.

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KeyPathwayMiner is a software for de novo network enrichment, aka network modules. It combines multiple OMICS data sets with biological networks to turn your expression, mutation, or association study into a systems biology story. It comes as Cytoscape app, as standalone software and as web service. It was downloaded >5000x from the Cytoscape app store alone and applied in various different biomedical settings.

TiCoNE is a software for the analysis of time course expression data (e.g. gene expression) together with biological networks. It will find time patterns emerging in the expression data and check for network modules enriched with genes of similar expression behavior over time. It comes as web server and as Cytoscape plugin.

Biclustering constrained by networks (BiCoN) is a powerful new systems medicine tool to stratify patients while elucidating disease mechanisms. BiCoN is a network-constrained biclustering approach which restricts biclusters to functionally related genes connected in molecular interaction networks and maximizes the expression difference between two groups of patients.

ProHarMeD (Proteomic Meta-Study Harmonization, Mechanotyping and Drug Repurposing Candidate Prediction) is a versatile tool designed to harmonize and compare proteomics data collected from multiple studies by its ability to perform ID and name conversionsbetween protein and gene levels, as well as across different organisms through ortholog mapping. It also aids in extracting disease mechanisms and identifying potential drug repurposing candidates.

Coronavirus Disease-2019 (COVID-19) is an infectious disease caused by the SARS-CoV-2 virus. It was first identified in Wuhan, China, and has since spread causing a global pandemic. Various studies have been performed to understand the molecular mechanisms of viral infection for predicting drug repurposing candidates. However, such information is spread across many publications and it is very time-consuming to access, integrate, explore, and exploit. We developed CoVex, the first interactive online platform for SARS-CoV-2 and SARS-CoV-1 host interactome exploration and drug (target) identification. CoVex integrates 1) experimentally validated virus-human protein interactions, 2) human protein-protein interactions and 3) drug-target interactions. The web interface allows user-friendly visual exploration of the virus-host interactome and implements systems medicine algorithms for network-based prediction of drugs. Thus, CoVex is an important resource, not only to understand the molecular mechanisms involved in SARS-CoV-2 and SARS-CoV-1 pathogenicity, but also in clinical research for the identification and prioritization of candidate therapeutics. CoVex renders COVID-19 drug research systems-medicine-ready by giving the scientific community direct access to network medicine algorithms integrating virus-host-drug interactions. We documented the CoVex development in our publication (download from Nature).

CoryneRegNet is the international reference database for corynebacterial transcriptional gene regulatory interactions.

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EhecRegNet is a database of gene regulations conserved between E. coli K12 and human pathogenic EHEC strains.

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PetriScape is a tool for discrete Petri net simulation of biological networks in Cytoscape.

GEDEVO is a method for pairwise network (graph) alignment using optimizing a global criterion: minimal graph edit distance, i.e. the minimal number of node/edge additions/deletions to transform one graph into another one. GEDEVO-M is an extension of GEDEVO for the alignment of multiple (>2) input networks. And NABEECO solves network alignment using Bee Colony Optimization.

CytoGEDEVO is a Cytoscape implementation of the pairwise network alignment algorithm of GEDEVO, which allows the direct integration of external node similarities (e.g. BLAST or graphlets)

Genes carry binding sites that allows specific microRNAs to repress their expression. According to the competing endogenous RNA hypothesis, genes also regulate each other in a competitive fashion via sponging microRNAs. We have contributed to the development of the (partial) correlation-based method SPONGE and the (conditional) mutual information-based method JAMI to infer such microRNA-mediated gene-gene interactions. In particular SPONGE is fast enough to infer ceRNA interactions genome-wide and thus facilitates the inference of a ceRNA regulatory network that can be used for hypothesis generation, biomarker detection and drug target discovery.