Biomedical Network Science Lab
Welcome to the Biomedical Network Science Lab!
The Biomedical Network Science (BIONETS) lab investigates molecular disease mechanisms using techniques from network science, combinatorial optimization, and artificial intelligence. We develop algorithms and tools to mine multi-omics data for such mechanisms and to individuate novel strategies for mechanistically grounded drug repurposing and causally effective treatments of complex diseases. We also develop privacy-preserving decentralized biomedical AI solutions, which enable cross-institutional studies on sensitive data. Finally, we are interested in meta-scientific questions such as reproducibility and the impact of data bias on biomedical AI systems.
In our paper "Emergence of power-law distributions in protein-protein interaction networks through study bias", we show that biased research interest in proteins and aggregation of interactions from multiple studies can explain why node degree distributions in PPI networks follow a power law.
In our paper "Spatial cell graph analysis reveals skin tissue organization characteristic for cutaneous T cell lymphoma", we present the Python tool SHouT to quantify tissue heterogeneity based on spatial omics data and use it to identify skin tissue patterns separating CTCL from benign conditions.
In our paper "DysRegNet: Patient-specific and confounder-aware dysregulated network inference towards precision therapeutics", we present a statistical model and Python tool to mine gene expression data for gene dysregulation events in individual samples in comparison to a control cohort.
In our paper "Network medicine-based epistasis detection in complex diseases: ready for quantum computing", we combine combinatorial optimization on graphs with quantum computing to identify SNPs involved in epistatic interactions.
In our paper "Guiding questions to avoid data leakage in biological machine learning applications", we present 7 questions that should be asked to prevent data leakage when constructing machine learning models in biological domains.
