Claassen Group

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We aim at elucidating the composition of heterogeneous cell populations and how these implement function in the context of cancer and immune biology by jointly evaluating single cell and genome wide measurements. Read more

Group News


Anna's paper accepted in PLOS Computational Biology.

Sparse Regression Based Structure Learning of Stochastic Reaction Networks from Single Cell Snapshot Time Series Read more 


Justins' paper came out in Cell Systems

Analysis of Cell Lineage Trees by Exact Bayesian Inference Identifies Negative Autoregulation of Nanog in Mouse Embryonic Stem Cells Read more 


Claassen Lab Semester Project: Deep Learning for dynamic system motif detection in health and disease

Cells process external and internal biochemical signals by means of signaling cascades, which constitute complex, dynamical networks. In this project we use novel deep (machine) learning techniques to decompose such networks in their building blocks in order to elucidate the overall structure. Read more 


Claassen Group: Causality from single-cell data

Causality inference from single-cell data. Inferring cause and effect, instead of mere correlative relationships between biological variables is a challenge of interest in many biological applications. This project deals with analyzing intervention experiments with single cell readout to infer causal signaling relationships. Read more 


Research Assistant/PhD student

A position is open for a research assistant/PhD student in the Computational Biology Group at the Institute of Molecular Systems Biology at ETH Zürich. The available position is offered in Claassen group that focuses on concepts from statistics, machine learning and mathematical optimization to describe biological systems from single cell data. We are looking for you as of April 15, 2015, or upon agreement, as a Research Assistant / PhD student Read more 

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