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Rätsch Lab: Machine Learning in Biology

We are interested in modern machine learning techniques suitable for the analysis of problems arising in genome biology and medicine, in particular cancer. We develop new learning techniques that

  • are capable of dealing with large amounts of genomic/medical data,
  • allow for very accurate predictions on the phenomenon at hand and
  • are able to comprehensibly provide reasons for their prognoses, and thereby assist in gaining new insights.

Currently we work on accurate methods for

  • RNA-seq-based transcriptome transcriptome reconstruction and analysis,
  • on reannotating of families of genomes of a species and
  • mapping expression as well as splicing QTLs.

We are furthermore interested in clinical data analysis and their integration with deep sequencing data.


Projects are supported by the European Union, the German Research Foundation, the Sloan-Kettering Institute and the Max-Planck Society.


We have several open postdoctoral positions (Details).



Please also check out our Galaxy-based Compute Services.
Group Photo 2011
The group at a retreat in March 2011.

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