Data Science in Biomedicine

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.

Data Science in Biomedicine

Members

Avatar

Jesse Angelis

Student Research Assistant

Avatar

Lisa-Marie Bente

PhD Student

Avatar

Mara Bretthauer

Student Research Assistant

Avatar

Daniel Dehncke

PhD Students

Avatar

Emetis Niazmand

Research Associate

Avatar

Kerem Karasu

Systems Administrator

Avatar

Vinzenz Fiebach

Student Research Assistant

Avatar

Gihanna Galindez

PhD Student

Avatar

Marvin Garske

Student Research Assistant

Avatar

Gordon Grabert

PhD Student

Avatar

Tim Kacprowski

Director

Avatar

Leon Kalix

PhD student

Avatar

Daniel Kusuma

Student Research Assistant

Avatar

Simone Scharke

Secretary

Avatar

Joschka Weikum

Student Research Assistant

Avatar

Roya Shiasi Sardoabi

Research Assistant

Avatar

Corinna Thoben

Student Research Assistant

Avatar

Tristen Vaeckenstedt

Student Research Assistant

Meet Our Alumni

View Alumni

Projects


PRETTY

PRETTY

The PRETTY (Personalised Prediction of Transplant Toxicity) project aims to develop a personalized prediction model to assess the risk of serious kidney damage following stem cell transplants in leukaemia patients. Its goal is to reduce the risk factors and thereby increase the lifespan and quality of life of those affected.

Sys_CARE

Sys_CARE

Systems Medicine Investigation of Alternative Splicing in Cardiac and Renal Diseases.

REPO-TRIAL

REPO-TRIAL

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.

FeatureCloud

FeatureCloud

The EU H2020 project FeatureCloud aims at developing methods for privacy-preserving, federated machine learning.

Network-Based Epistasis Detection

Network-Based Epistasis Detection

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.

De novo network enrichment

De novo network enrichment

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.

Software & Resources

Epistasis Disease Atlas

Epistasis Disease Atlas

The EDA is a comprehensive database focused on epistatic interactions, analyzing how groups of genes work together to cause complex diseases. The atlas currently covers Alzheimer’s, rheumatoid arthritis, bipolar disorder, coronary heart disease, inflammatory bowel disease, type 1 and type 2 diabetes, and hypertension. Data is accessible through a user-friendly R-Shiny app for searching, visualization, and analysis.

Spycone

Spycone is a python package for a splicing-aware analysis of time course data. It is available on Github.

DICAST

DICAST

DICAST comprises several alternative splicing event detection tools for analyzing RNA-Seq data. It can be used as a benchmark to compare the results of the different tools. More information and instructions can be found here.

NeDRex

NeDRex

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.

ASimulatoR

ASimulatoR is an R package for the simulation of RNA-seq reads with alternative splicing events. It is freely available on GitHub.

NEASE

NEASE

NEASE (Network-based Enrichment method for Alternative Splicing Events) is a Python package for the functional enrichment of alternative splicing events. The tool is available on GitHub.

DIGGER

DIGGER

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.

CoVex

CoVex

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.

Scellnetor

Scellnetor

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.

EpiGEN

EpiGEN

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

Fastlogranktest is a software package providing wicked-fast implementations of the logrank test in C++, R, and Python.

BiCoN

BiCoN

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.

Recent Publications