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.
- 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
- Machine learning: Deep learning, dimensionality reduction, clustering, classification
- Statistical inference: Probabilistic models, network inference
- Mathematical modeling: Stochastic hybrid models, Boolean networks
- Tumor heterogeneity
- Leading drivers of cancer
- Molecular mechanisms
- Patient stratification
- Early diagnosis
- Targeted treatment
Identifying the sources of cell heterogeneity is crucial to developing effective disease management strategies.
Multimodal data integration
Developing a predictive computational technology to exploit and integrate multiple molecular and clinical data.
Signaling network reconstruction
Reconstructing the wiring diagram of cell signaling networks by statistical means.
Constructing accurate models to predict macroscopic molecular parameters.
Molecular fingerprints of cancer
Developing a novel computational framework to construct a phenotype–genotype association network for prostate cancer.
Prediction of anticancer compound sensitivity with multi-modal attention-based neural networks.
We gratefully acknowledge generous funding from