Research Group Christof Seiler
We develop new statistical methods to analyze multi-omics data. We leverage prediction models from machine learning and make them fit for scientific purpose by quantifying their uncertainty. We build models that can learn biological causal mechanisms from experimental data. We are also active contributors of bioinformatics software packages and maintain several R packages on Bioconductor.
Our Projects
In the RAI project, we will build a platform that will help researchers in biomedicine to quantify uncertainty for their prediction models. We will use recent advances in statistics and machine learning—the so-called conformal prediction framework—to add prediction intervals to their models. The researchers will upload their models and part of their data on our platform website. We will then build prediction intervals for their models and deliver them back to researchers as a download.
Selected Publications
- Guazzini, M., Reisach, A.G., Weichwald, S., and Seiler, C. spillR: Spillover Compensation in Mass Cytometry Data. Bioinformatics (2024).
