Domain Interaction Graph Guided ExploreR (DIGGER) integrates protein-protein interactions and domain-domain interactions into a joint graph and maps interacting residues to exons. DIGGER allows the users to query exons or isoforms individually or as a set to visually explore their interactions.
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/).
Systems Medicine Investigation of Alternative Splicing in Cardiac and Renal Diseases.
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.