Computational systems biology

Developing predictive models for precision medicine

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
Genomics overview


  • Machine learning: Deep learning, dimensionality reduction, clustering, classification
  • Statistical inference: Probabilistic models, network inference
  • Mathematical modeling: Stochastic hybrid models, Boolean networks

Research goals

  • Tumor heterogeneity
  • Leading drivers of cancer
  • Molecular mechanisms
Genomics overview

Personalized medicine

  • Patient stratification
  • Early diagnosis
  • Targeted treatment
Cell heterogeneity

Tumor hetero­geneity

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.


Biomarker dis­covery

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.

PaccMann logo


Prediction of anti­cancer com­pound sen­si­tivity with multi-modal atten­tion-based neural networks.

Ask the expert


Maria Rodriguez Martinez

María Rodríguez Martínez
IBM Research scientist

We gratefully acknowledge generous funding from SNF and EU logos