Statistics and Computational Biology
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Timeline of methods developed in our group
(click image for a larger view)
Our main research topics
in biostatistics and bioinformatics are:
Additional current interests include the analysis of RNA-Seq data, shrinkage methods and logical probability.
Most of our methods are available in freely available software.
We have also been involved
in organizing a number of conferences.
Please find below a representative selection of our publications.
For a complete list of publications and for current preprints see the publications of Korbinian Strimmer
and the web pages of the other group members.
Transcriptome and proteome analysis:

Example of a protein mass spectrum
(click image for a larger view)
We are interested in statistical bioinformatics approaches
to analyze gene expression an proteomics data and
and also have developed a platform for computational mass spectrometry:
Gene ranking and biomarker discovery:

CAR regression models for diabetes data
(click image for a larger view)
We are interested biomarker discovery and have recently proposed
the CAT and CAR scores for ranking of correlated genes. In addition we introduced
the shrinkage t statistic, a regularized t-score useful in high-dimensional
data analysis with small samples:
Signal identification and FDR:

Local FDR thresholds and natural class boundary
(click image for a larger view)
We have developed statistical approaches for detection of signal
in high-dimension genomic data and for multiple testing using
false discovery rates (FDR):
Graphical models and biological networks:

Entropy-based gene association network
(click image for a larger view)
In our group we have developed a series of algorithms using graphical
models for learning large-scale gene association networks from high-throughput
data:
-
Introduction to graphical modelling.
M. Scutari and K. Strimmer. 2011.
Chapter 11 in: M. P. H. Stumpf, D. J. Balding, and M. Girolami (eds.).
Handbook of Statistical Systems Biology. Wiley, Chichester, UK, pp. 237-254.
(arXiv:1005.1036)
-
Entropy inference and the James-Stein estimator, with application
to nonlinear gene association networks.
J. Hausser and K. Strimmer. 2009.
J. Mach. Learn. Res. 10: 1469-1484.
(arXiv:0811.3579)
-
From correlation to causation networks: a simple approximate
learning algorithm and its application to high-dimensional plant
gene expression data.
R. Opgen-Rhein and K. Strimmer. 2007.
BMC Syst. Biol.
1: 37.
-
Learning causal networks from systems biology time course
data: an effective model selection procedure for the
vector autoregressive process.
R. Opgen-Rhein and K. Strimmer. 2007.
BMC Bioinformatics 8
Suppl. 2: S3.
-
Inferring gene dependency networks from genomic longitudinal data:
a functional data approach.
R. Opgen-Rhein and K. Strimmer. 2006.
REVSTAT
4:53-65.
-
Reverse engineering genetic networks using the GeneNet package.
J. Schäfer, R. Opgen-Rhein, and K. Strimmer. 2006.
R News
6/5:50-53.
-
A shrinkage approach to large-scale covariance matrix
estimation and implications for functional genomics.
J. Schäfer and K. Strimmer. 2005.
Statist. Appl.
Genet. Mol. Biol. 4: 32.
-
An empirical Bayes approach to inferring large-scale gene association
networks.
J. Schäfer and K. Strimmer. 2005.
Bioinformatics
21: 754-764.
Molecular evolution:

Reversible jump MCMC estimate of population size
(click image for a larger view)
One of our first research interests were methods for
phylogenetic analysis and population genetics using sequence data:
-
Inference of demographic history from genealogical trees using reversible
jump Markov chain Monte Carlo.
R. Opgen-Rhein, L. Fahrmeir, and K. Strimmer. 2005.
BMC Evol. Biol.
5: 6.
-
APE: Analyses of phylogenetics and evolution in R language.
E. Paradis, J. Claude, and K. Strimmer. 2004.
Bioinformatics
20: 289-290.
- Inferring
confidence sets of possibly misspecified gene trees.
K. Strimmer and A. Rambaut. 2002.
Proc. R. Soc.
Lond. B 269: 137-142.
-
Exploring the
demographic history of DNA sequences using the generalized skyline plot.
K. Strimmer and O. G. Pybus. 2001.
Mol. Biol. Evol.
18: 2298-2305.
-
Likelihood mapping: a simple method to visualize phylogenetic content of a sequence
alignment.
K. Strimmer and A. von Haeseler. 1997.
Proc. Natl. Acad. Sci. USA
94: 6815-6819.
-
Quartet puzzling: a quartet maximum likelihood method for reconstructing
tree topologies.
K. Strimmer and A. von Haeseler. 1996.
Mol. Biol. Evol.
13: 964-969.
(publisher PDF is defective - instead use this scan)
Conferences:
Our group was coorganizer of the workshop
Complex Stochastic Systems in Biology and Medicine 2004 in Munich and we have hosted the Computational Systems Biology (WCSB 2008) conference in Leipzig. More recently,
we helped to organize the life science session
at GOCPS 2010.
Last modified:
September 12, 2012