Computational network biology for integrative analysis of human diseases
Herwig Lab
Our research focuses on developing computational methods for (i) analyzing molecular
data, particularly in human diseases such as cancer and type 2 diabetes, and (ii) integrating these data within biological networks. We developed NetCore, a novel network propagation framework, to extract mechanistic insights from heterogeneous multi-omics data for biomedical applications. Additionally, by combining NetCore with machine learning, we are enhancing the interpretability of “black-box” predictions by adding biological plausibility. A key element of the network approaches is the use of stable molecular interaction networks, provided through ConsensusPathDB, our resource for extracting high-quality interactions from numerous public databases. Maintained by our group since 2009, ConsensusPathDB has accumulated over 2,700 citations to date.
We also developed IsoTools, a software package for long-read transcriptome sequencing (Pac- Bio and Oxford Nanopore), to refine existing gene expression models and investigate alternative splicing. In collaboration with external clinical partners, IsoTools has been used to analyze splicing factor mutations and their role in aberrant splicing regulation in blood cancer patients.
During the reporting period (2022–2024), our lab published 18 scientific publications.
