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 machine learning and computational biology
Understanding real-world datasets
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
Anticancer drug modelling for precision medicine.
INtERAcT
Automatic text mining and analysis.
PIMKL
Pathway-induced multiple kernel learning.
CellCycleTRACER
A novel computational method to quantify cell cycle and cell volume variability.
Chimaera
Estimating the frequency of genetic alterations.
COSIFER
Consensus inference of molecular networks.
