Research Group @ PLRI @ TU Braunschweig
Our group aims to elucidate the molecular mechanisms behind phenotypes and diseases. To that end, we develop integrative bioinformatics methods leveraging network analysis, machine learning, and statistics. We apply own and existing approaches in close collaboration with biologists and physicians to derive insights from multi-omics data.
Systems Medicine for the Development of Novel Theranostic Approaches for Oncological and Immunological Diseases
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.
The EU H2020 project FeatureCloud aims at developing methods for privacy-preserving, federated machine learning.
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.
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. More information and current updates can be found at the CoVex blog at the Chair of Experimental Bioinformatics website.
EpiGEN is a Python pipeline for simulating epistasis data. It supports epistasis models of arbitrary size, which can be specified either extensionally or via parametrized risk models. Moreover, the user can specify the minor allele frequencies (MAFs) of both noise and disease SNPs, and provide a bias target distribution for the generated phenotypes to simulate observation bias. EpiGEN is freely available as python 3 package on GitHub.
Fastlogranktest is a software package providing wicked-fast implementations of the logrank test in C++, R, and Python.
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.