Big Data Analysis and Biomedical Research meet in our lab: We develop novel Data Mining Algorithms to detect patterns and statistical dependencies in large datasets from Biology and Medicine.
We try to reach two grand goals: To enable the automatic generation of new knowledge from Big Data through Machine Learning, and to gain an understanding of the relationship between the function of Biological Systems and their molecular properties. This understanding is of fundamental importance for Personalized Medicine, which tailors medical treatment to the molecular properties of each patient.
New publication: Genome-wide genetic heterogeneity discovery with categorical covariates
Felipe, Laetitia, Dean, Damian and Karsten have developed an alternative to burden tests for genetic heterogeneity discovery, which searches the entire genome rather than preselected genomic regions.