NeDRex is an integrative and interactive platform for network-base drug repurposing and disease module identification. More information, tutorials and a download link for the app can be found [here](https://nedrex.net/index.html).
A new *Reference Module in Biomedical Science* "Multi-Omics Analysis in a Network Context" is now available on *Elsevier*.
To address the pandemic of the Coronavirus Disease-2019 (COVID-19), drug repurposing can be a helpful approach since it offers the possibility to find alternative fields of application for already approved drugs. **CoVex** is the first network and systems medicine online data analysis platform that integrates virus-human interaction data for SARS-CoV-2 and SARS-CoV. It is available as [interactive webtool](https://exbio.wzw.tum.de/covex/). More information and current updates can be found at the [CoVex blog](https://www.baumbachlab.net/exbio-vs-covid-part-1) at the *Chair of Experimental Bioinformatics* website.
Scellnetor is a novel scRNA-seq clustering tool. It allows the analysis of pseudo time-courses in single-cell sequencing data via a network-constrained clustering algorithm. Scellnetor is available as interactive online application at the [Scellnetor website](https://exbio.wzw.tum.de/scellnetor/).
BiCoN is a powerful new systems medicine tool to stratify patients while elucidating the responsible 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 subgroups of patients. A package for network-constrained biclustering of patients and multi-omics data can also be used. Download and installation instructions can be found [here](https://pypi.org/project/bicon/).
The EU H2020 project REPO-TRIAL aims at developing an _in silico_ approach to optimise the efficacy and precision of drug repurposing trials. To this end we integrate heterogeneous data into a comprehensive interactome of disease-drug-gene interactions (a new diseasome) and develop graph-based machine learning approaches to investigate this highly complex data.
We tackle the challenge of higher-order epistasis detection using biological networks to narrow the search space and GPU computing to improve the efficiency. Phenotype-specific epistasis-modules extracted from larger networks will help to better understand the underlying biological mechanisms of different phenotypes.
We develop tools that leverage information from molecular interaction networks in understanding molecular profiling data. _De novo_ network enrichment tools extract subnetworks that mechanistically explain a phenotype of interest, e.g. a disease.