Rätsch Lab: Machine Learning in Biology

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

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

Currently we consider novel methods for ab initio gene finding in eukaryotic genomes. Moreover, we work on the prediction of alternative splicing and the biological verification of our predictions.

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