Statistics and Computational Biology

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Please find below all publications coauthored by Korbinian Strimmer. For additional publications of group members please visit the respective homepages. You may also be interested in a shortlist of ten selected papers.

2012 | 2007-2011 | 2002-2006 | 1994-2001

2012:

2007-2011:

• Signal identification for rare and weak features: higher criticism or false discovery rates?
  B. Klaus and K. Strimmer. 2011.
  (arXiv:1112.2615)
• 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.
  (arXiv:1005.1036)
• Learning false discovery rates by fitting sigmoidal threshold functions.
  B. Klaus and K. Strimmer. 2011.
  Journal de la Société Française de Statistique 152: 39-50.
  (reprint, arXiv:1104.5414)
• This regression approach is available in the care R package. High-dimensional regression and variable selection using CAR scores.
  V. Zuber and K. Strimmer. 2011.
  Statist. Appl. Genet. Mol. Biol. 10: 34.
  (reprint, arXiv:1007.5516)
• See the MALDIquant R package. Analysis of proteomic data using MALDIquant.
  S. Gibb and K. Strimmer. 2011.
  Proceedings of the 8th International Workshop on Computational Systems Biology, WCSB 2011 (June 6-8, 2011, Zurich, Switzerland), pp. 49-52.
  (reprint)
• Over-optimism in bioinformatics: an illustration.
  M. Jelizarow, V. Guillemot, A. Tenenhaus, K. Strimmer, and A.-L. Boulesteix. 2010.
  Bioinformatics 26: 1990-1998.
  (reprint)
• Thresholding methods for feature selection in genomics: higher criticism versus false non-discovery rates.
  B. Klaus and K. Strimmer. 2010.
  Proceedings of the 7th International Workshop on Computational Systems Biology, WCSB 2010 (June 16-18, 2010, Luxembourg, Luxembourg), pp. 59-62.
  (reprint)
• See the sda R package. Feature selection in omics prediction problems using cat scores and false non-discovery rate control.
  M. Ahdesmäki and K. Strimmer. 2010.
  Ann. Appl. Stat. 4: 503-519
  (reprint, arXiv:0903.2003)
• The cat score is implemented in the sda and st R packages. Gene ranking and biomarker discovery under correlation.
  V. Zuber and K. Strimmer. 2009.
  Bioinformatics 25: 2700-2707.
  (reprint, arXiv:0902.0751)
• This method is implemented in the entropy R package. 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.
  (reprint, arXiv:0811.3579)
• A general modular framework for gene set enrichment analysis.
  M. Ackermann and K. Strimmer. 2009.
  BMC Bioinformatics 10: 47.
  (reprint)
• Comparison of the Gaussian process classification approach with classical penalized classification methods in high-dimensional omics data.
  M. Ahdesmäki and K. Strimmer. 2009.
  Proceedings of the 6th International Workshop on Computational Systems Biology, WCSB 2009 (June 10-12, 2009, Aarhus, Denmark), pp. 19-22.
  (reprint)
• Correlation-adjusted t-scores in application to functional magnetic resonance imaging data.
  V. Zuber and K. Strimmer. 2009.
  Proceedings of the 6th International Workshop on Computational Systems Biology, WCSB 2009 (June 10-12, 2009, Aarhus, Denmark), pp. 163-166.
  (reprint)
• Genetic distances and nucleotide substitution models: Theory.
  K. Strimmer and A. von Haeseler. 2009.
  Chapter 4 in: P. Lemey, M. Salemi, A.M. Vandamme (eds.). The Phylogenetic Handbook 2nd edition. Cambridge University Press, Cambridge, UK.
  (manuscript 2nd edition)
• A unified approach to false discovery rate estimation.
  K. Strimmer. 2008.
  BMC Bioinformatics 9: 303.
  (reprint, typographic corrections)
• Visit the fdrtool homepage. fdrtool: a versatile R package for estimating local and tail area- based false discovery rates.
  K. Strimmer. 2008.
  Bioinformatics 24: 1461-1462.
  (reprint)
• Connections between the augmented bootstrap and the shrinkage covariance estimator. Comment on: Augmenting the bootstrap to analyze high- dimensional data.
  K. Strimmer. 2008.
  TEST 17: 25-27.
  (reprint)
• 
  Download the WCSB 2008 proceedings. Proceedings of the 5th International Workshop on Computational Systems Biology, WCSB 2008, June 11-13, 2008, Leipzig, Germany.
  M. Ahdesmäki, K. Strimmer, N. Radde, J. Rahnenführer, K. Klemm, H. Lähdesmäki, and O. Yli-Harja (eds). 2008.
  (reprint)
• Therapeutic vaccination reduces HIV sequence variability.
  D. Hoffmann, J. Seebach, A. Cosma, F. D. Goebel, K. Strimmer, H. M. Schätzl, and V. Erfle. 2008.
  FASEB J. 22:437-444.
  (reprint)
• 
  This method is available in the GeneNet R package. 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.
  (reprint)
• 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.
  (reprint)
• The "shrinkage t" statistic is implemented in the st R package. Accurate ranking of differentially expressed genes by a distribution- free shrinkage approach.
  R. Opgen-Rhein and K. Strimmer. 2007.
  Statist. Appl. Genet. Mol. Biol. 6: 9.
  (reprint, typographic corrections)
• The PLS analyzes were conducted using the R package plsgenomics. Partial least squares: a versatile tool for the analysis of high- dimensional genomic data.
  A.-L. Boulesteix and K. Strimmer. 2007.
  Briefings in Bioinformatics. 8: 32-44.
  (reprint)

2002-2006:

• Inferring gene dependency networks from genomic longitudinal data: a functional data approach.
  R. Opgen-Rhein and K. Strimmer. 2006.
  REVSTAT 4:53-65.
  (reprint)
• Using regularized dynamic correlation to infer gene dependency networks from time-series microarray data.
  R. Opgen-Rhein and K. Strimmer. 2006.
  Proceedings of the 4th International Workshop on Computational Systems Biology, WCSB 2006 (June 12-13, 2006, Tampere, Finland), pp. 73-76.
  (reprint)
• Reverse engineering genetic networks using the GeneNet package.
  J. Schäfer, R. Opgen-Rhein, and K. Strimmer. 2006.
  R News 6/5:50-53.
  (reprint)
• Book review of "Statistical Methods in Bioinformatics: An Introduction", 2nd edition, Springer 2005, by W. J. Ewens and G. R. Grant.
  K. Strimmer. 2006.
  Biometr. J. 48:178-179
  (reprint)
• See the corpcor R package for efficient estimation of large-scale covariance matrices and their inverses. 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.
  (reprint)
• Predicting transcription factor activities from combined analysis of microarray and ChIP data: a partial least squares approach.
  A.-L. Boulesteix and K. Strimmer. 2005.
  Theor. Biol. Med. Model. 2: 23.
  (reprint)
• See the GeneNet R package. An empirical Bayes approach to inferring large-scale gene association networks.
  J. Schäfer and K. Strimmer. 2005.
  Bioinformatics 21: 754-764.
  (reprint)
• For rjMCMC inferences from DNA sequences use the ape software. 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.
  (reprint)
• This paper reviews procedures for GGM network inference. Learning large-scale graphical Gaussian models from genomic data.
  J. Schäfer and K. Strimmer. 2005.
  AIP Conf. Proc. 776: 263-276.
  (reprint)
• TREEFINDER: A powerful graphical analysis environment for molecular phylogenetics.
  G. Jobb, A. von Haeseler, and K. Strimmer. 2004.
  BMC Evol. Biol. 4: 18.
  (reprint)
• APE: Analyses of phylogenetics and evolution in R language.
  E. Paradis, J. Claude, and K. Strimmer. 2004.
  Bioinformatics 20: 289-290.
  (reprint)
• Periodicity testing is available in the GeneCycle R package. Identifying periodically expressed transcripts in microarray time series data.
  S. Wichert, K. Fokianos, and K. Strimmer. 2004.
  Bioinformatics 20: 5-20.
  (reprint, corrigendum)
• A brief introduction to the phylogenetic analysis library, version 1.5.
  M. Goode, K. Strimmer, A. Drummond, E. Buckler, and A. Rodrigo. 2004.
  Pp. 175-179 in: Y.-P. Phoebe Chen (Ed.). CRPIT Volume 29: Proceedings of the Second Asia-Pacific Bioinformatics Conference (APBC2004), Dunedin, NZ, January 2004.
  (reprint)
• Inference and applications of molecular phylogenies: an introductory guide.
  K. Strimmer and D. L. Robertson. 2004.
  Chapter 5 in: C. E. Sansom and R. M. Horton (eds.), The Internet for Molecular Biologists (Practical Approach Series). Oxford University Press, Oxford, UK.
  (manuscript)
• A CART-based approach to discover emerging patterns in microarray data.
  A.-L. Boulesteix, G. Tutz, and K. Strimmer. 2003.
  Bioinformatics 19: 2465-2472.
  (reprint)
• A novel exploratory method for visual recombination detection.
  K. Strimmer, K. Forslund, B. Holland, and V. Moulton. 2003.
  Genome Biology 4: R33.
  (reprint)
• Modeling gene expression measurement error: a quasi-likelihood approach.
  K. Strimmer. 2003.
  BMC Bioinformatics 4: 10.
  (reprint)
• Nucleotide substitution models.
  K. Strimmer and A. von Haeseler. 2003.
  Chapter 4 in: M. Salemi, A.M. Vandamme (eds.). The Phylogenetic Handbook. Cambridge University Press, Cambridge, UK.
  (manuscript 1st edition, see also 2nd edition 2009)
• Phylogeny inference based on maximum-likelihood methods with TREE-PUZZLE.
  A. von Haeseler and K. Strimmer. 2003.
  Chapter 6 in: M. Salemi, A.M. Vandamme (eds.). The Phylogenetic Handbook. Cambridge University Press, Cambridge, UK.
  (manuscript)
• Comment on: Bayesian phylogenetic inference from animal mitochondrial genome rearrangements.
  K. Strimmer. 2002.
  J. R. Statist. Soc. B 64: 745.
  (reprint)
• TREE-PUZZLE: Maximum likelihood phylogenetic analysis using quartets and parallel computing.
  H. A. Schmidt, K. Strimmer, M. Vingron, and A. von Haeseler. 2002.
  Bioinformatics 18: 502-504.
  (reprint)
• Inferring confidence sets of possibly misspecified gene trees.
  K. Strimmer and A. Rambaut. 2002.
  Proc. R. Soc. Lond. B 269: 137-142.
  (reprint)

1994-2001:

• The generalized skyline plot is implemented in ape. 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.
  (reprint)
• PAL: An object-oriented programming library for molecular evolution and phylogenetics.
  A. Drummond and K. Strimmer. 2001.
  Bioinformatics 17: 662-663.
  (reprint)
• Recombination analysis using directed graphical models.
  K. Strimmer, C. Wiuf, and V. Moulton. 2001.
  Mol. Biol. Evol. 18: 97-99.
  (reprint)
• Dating the common ancestor of SIVcpz and HIV-1 group M and the origin of HIV-1 subtypes using a new method to uncover clock-like molecular evolution.
  M. Salemi, K. Strimmer, W. W. Hall, M. Duffy, E. Delaporte, S. Mboup, M. Peeters, and A.-M. Vandamme. 2001.
  FASEB J. 15:276-278.
  (reprint, full text version)
• Likelihood analysis of phylogenetic networks using directed graphical models.
  K. Strimmer and V. Moulton. 2000.
  Mol. Biol. Evol. 17: 875-881.
  (reprint)
• Maximum likelihood methods in molecular phylogenetics.
  K. Strimmer. 1997.
  PhD Thesis. University of Munich. Herbert Utz Verlag.
  (manuscript)
• 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.
  (reprint)
• Bayesian probabilities and quartet puzzling.
  K. Strimmer, N. Goldman, and A. von Haeseler. 1997.
  Mol. Biol. Evol. 14: 210-211.
  (reprint)
• The TREE-PUZZLE program implements "likelihood mapping" and "quartet puzzling" (among other things). Quartet puzzling: a quartet maximum likelihood method for reconstructing tree topologies.
  K. Strimmer and A. von Haeseler. 1996.
  Mol. Biol. Evol. 13: 964-969.
  (reprint)
• Accuracy of neighbor joining for n-taxon trees.
  K. Strimmer and A. von Haeseler. 1996.
  Sys. Biol. 45: 514-521.
  (reprint)
• Modell-Theorie der Dynamik der Raum-Zeit auf der Basis unimodularer Drei-Metriken.
  K. Strimmer. 1994.
  Diploma Thesis. University of Munich.
  (manuscript)

 

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