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 are working towards two central 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.
GitHub
The source code and data sets of our research projects can be downloaded from our external page GitHub repository. More information on the individual projects can be found here.
Significant Pattern Mining
We develop methods that discover significant patterns in high dimensional datasets while being runtime efficient and statistically sound. Our algorithms can be applied to graphs or collections of sequences and allow to account for dependencies between objects, to control the Family-Wise Error Rate and to correct for categorical covariates. More about our work on Significant Pattern Mining can be found here.
MLFPM
We are coordinating the Marie Curie Initial Training Network "Machine Learning Frontiers in Precision Medicine" (external page MLFPM).
easyGWAS
easyGWAS Our online platform for computing, storing, sharing, analyzing and comparing the results of genome-wide association studies.
The "grand finale" of our Innovative Training Network on "Machine Learning Frontiers in Precision Medicine" is taking place in person at the Max Planck Institute of Psychiatry in Munich on October 18 and 19, 2022.
On September 23, 2022, Max defended his excellent PhD thesis entitled "Representation Learning for Dimensionality Reduction, Irregularly-Sampled Sequences and Graphs".
New lab member
Philip Hartout (ETH Zürich) joins the group as PhD student. Welcome!
The project "Personalized, data-driven prediction and assessment of infection-related outcomes in Swiss ICUs (IICU)", led by Karsten Borgwardt (ETHZ) and Adrian Egli (University Hospital Basel), has been selected as National Data Stream. SPHN and PHRT will jointly fund the project with 5 mio. CHF.
In their latest publication, Caroline, Karsten and their collaborators from the University Hospital Basel developed Machine Learning methods for MALDI-TOF mass spectrometry data to detect antibiotic resistance. This work has now been published in Nature Medicine.
