Research interests:

Overview of methods developed in the group
Timeline of methods developed in our group (click image for a larger view)

Our research focuses on statistical and machine learning methods and models for high-dimensional data and to understand the underlying principles of learning from data.

We have developed computationally effective methods for high-dimensional regularized estimation using shrinkage methods, for model and variable selection, for signal identification, for computing false discovery rates and performing dimension reduction, for learning graphical models and for regularized classification and regression.

Biological applications of our methods have included the analysis of transcriptomics and proteomics data, gene ranking and biomarker discovery, construction of biological networks and trees as well as modeling of molecular evolution.

Korbinian Strimmer is recognized as "Highly Cited Researcher".

Our aim is to make all our work accessible and reproducible by providing corresponding free and open source software as well as analysis scripts and data sets.


R packages - statistical methods:

big data toolbox
Statistical toolbox for analyzing high-dimensional data (click image for a larger view)

R packages - computational biology:

Matlab/Octave code:

This code was kindly contributed by Kevin P. Murphy.


Data sets:

Most of the data provided below are packaged as part of an R package. For further information concerning the raw data please consult the original data authors and papers.

From Jendoubi and Strimmer, 2019 (CCA data integration):

From Gibb and Strimmer, 2015 (differential proteomics):

From Zuber and Strimmer, 2011 (CAR score):

From Zuber and Strimmer, 2009 (CAT score):

From Opgen-Rhein and Strimmer, 2007a (VAR network) and 2007b (approximate causal network):

From Opgen-Rhein and Strimmer, 2007 (shrinkage t statistic):

From Boulesteix and Strimmer, 2005 (PLS transcription factor prediction):

From Wichert, Fokianos and Strimmer, 2004 (identification of cell cycle genes):

From Strimmer, 2003 (quasi-likelihood approach):

From diverse other papers:


Our group was organizer of the meeting Statistical Methods for Postgenomic Data (SMPGD 2017) at Imperial College London. Previously, we helped organizing the life science session at GOCPS 2010 and the Computational Systems Biology (WCSB 2008) conference at the University of Leipzig. At LMU Munich our group was coorganizer of the workshop Complex Stochastic Systems in Biology and Medicine 2004.