With the advances of high-throughput experimental techniques, biomedical research is turning into information science. This requires the use of machine and deep-learning approaches, statistics and mathematical modelling. Individual cellular processes that comprise the interplay of several molecular players, such as cell signaling, can now be quantitatively characterized to allow a systematic view of biological processes. A better understanding of biological processes is crucial in order to provide robust predictive models that improve disease prognoses and treatment strategies.
Our group is exploiting a large variety of data — multi-omics datasets, single-cell proteomics and mass spectrometry-based quantitative proteomics — to dissect the molecular mechanisms of cancer. Our goal is to develop predictive models for precision medicine.
Data
- Omics: RNAseq, CNV, SNP, miRNA, SWATH–MS
- Single cell: Mass cytometry
- Clinical data: Survival outcome, treatment
- Literature: Publications
- Compound structure: SMILES, graph representation of molecules
- Networks: protein–protein interactions, pathways
Methods
- Machine learning: Deep learning, dimensionality reduction, clustering, classification, generative models
- Statistical inference: Probabilistic models, network inference
- Mathematical modeling: Stochastic hybrid models, Boolean networks
Research goals
- Tumor heterogeneity
- Leading drivers of cancer
- Molecular mechanisms
Personalized medicine
- Patient stratification
- Early diagnosis
- Targeted treatment
