- Large-scale Machine Learning. The group has a long history of developing large-scale learning methods to classify sequences, predicting sequence annotations and solving large optimization problems.
- Accurate transcriptome reconstruction. The lab has developed a host of methods to accurately reconstruct, quantify and characterize transcriptomes from RNA-Seq data. In collaborations, the techniques are used to understand mechanisms of co- & post-transcriptional regulation.
- Identification of RNA-processing regulators. The lab is leading efforts to discover trans-acting factors of RNA-processing regulation via association studies. We analyze ≈4,000 cancer exomes & transcriptomes to identify factors associated with splicing and other changes.
- Clinical decision support systems. The group develops innovative methods for the analysis of electronic health records (EHR) and pathology slides. The overall aim is to build elements of decision support systems that utilizes EHR, image and genomic information to provide suggestions for treatments and to assist in the design of new trials.
- We have openings for multiple positions in the lab. Check out the opportunities page.
- The lab has moved to ETH Zürich on May 2016 to start the Biomedical Informatics group. Six group member from the NYC group joined the move, three stayed in New York to either complete their Ph.D. there or to join a startup.
- We have updated the group web pages. Stay tuned…!
Welcome to the Biomedical Informatics Lab of Prof. Gunnar Rätsch
We are interested in modern machine learning techniques suitable for the analysis of problems arising in medicine and 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 phenomenon at hand and are able to comprehensibly provide reasons for their prognoses, and thereby assist in gaining new biomedical insights.
Current Research Topics: