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Machine Learning and Computational Biology Lab

Prof. Karsten Borgwardt's lab works at the interface of Big Data Analysis and Systems Biology: The lab develops efficient data mining algorithms and mathematical models for detecting patterns and statistical dependencies in large datasets from biology and medicine. Its grand research goals are to enable the automatic generation of new knowledge from big data through Machine Learning, and to explore the relationship between the functions of biological systems and their molecular features. This understanding is fundamental for Personalized Medicine, which tailors medical treatment to the molecular properties of each patient.

A prime example for Big Data problems in Biology is Statistical Genetics, which tries to discover the genetic basis of hereditary diseases. As individuals differ at millions of genomic loci, this search becomes an enormous computational challenge. At the same time, one faces an enormous multiple hypothesis testing problem due to the large number of candidate loci. The lab develops some of the most advanced methods for dealing with those challenges.