Computational systems biology

Developing predictive models for precision medicine

At IBM Research – Zurich, we develop novel approaches to analyze different molecular levels of high-throughput data. From single-cell to cell population-averaged data (proteomics, transcriptomics), we aim to integrate multiple layers of genome-scale information. This, in combination with clinical information and prior knowledge through literature mining, enables us to understand molecular mechanisms and explore applications to personalised medicine.

Our main research projects include, but not are limited to, studying cell-to-cell heterogeneity, integrative multi-omics analysis, dynamic network inference and robust biomarker discovery, most of which are applied in the case of cancer. Recently, we focused on anticancer drug modelling, specifically on leveraging biomarker information into generative models for de-novo drug design, attempting to bridge systems biology and anticancer drug discovery.

We gratefully acknowledge our numerous collaborations with university hospitals, research institutes and universities that work alongside our team in many of our projects.

Research topics

Interpretability for ML

Interpretability for machine learning and computational biology

Understanding real-world datasets

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.

Research assets

PaccMann logo


Anticancer drug modelling for precision medicine.



Automatic text mining and analysis.



Pathway-induced multiple kernel learning.

CellCycleTracer logo


A novel computational method to quantify cell cycle and cell volume variability.



Estimating the frequency of genetic alterations.



Consensus inference of molecular networks.

Ask the expert


Maria Rodriguez Martinez

María Rodríguez Martínez
IBM Research scientist

We gratefully acknowledge generous funding from SNF and EU logos