Christoph Lippert
Researcher, eScience
My research is focussed on machine learning and statistics applications in genomics and genetics.
My expertise lies mostly in Gaussian processes and linear mixed models.
In this space I have mostly been working on methods for statistical testing in genome-wide association studies (GWAS).
- Jointly with Bjarni Vilhjalmsson I am organizing a workshop on Heritability analysis and genetic trait prediction at ISMB in Berlin. See you there on Sunday, July 21, 2013.
- We are always looking for PhD students with a strong background in machine learning, statistics, or bioinformatics who are interested in internship projects in our office in Los Angeles. If you are interested, write me an e-mail.
- Thanks for the great feedback on our tutorial on genome-wide association studies at ECCB 2012 in Basel! (tutorial material)
Publications
2013
- C. Lippert, G. Quon, E. Y. Kang, C. M. Kadie, J. Listgarten, and D. Heckerman, The benefits of selecting phenotype-specific variants for applications of mixed models in genomics, in Scientific Reports, Nature Publishing Group, 9 May 2013
- J. Listgarten, C. Lippert, and D. Heckerman, FaST-LMM-Select for addressing confounding from spatial structure and rare variants, in Nature Genetics, Nature Publishing Group, 26 April 2013
- G. Quon, C. Lippert, D. Heckerman, and J. Listgarten, Patterns of methylation heritability in a genome-wide analysis of four brain regions, in Nucleic Acids Research, Oxford Univ Press, 2013
- C. Lippert, J. Listgarten, R.I. Davidson, J. Baxter, H. Poon, C. Kadie, and D. Heckerman, An Exhaustive Epistatic SNP Association Analysis on Expanded Wellcome Trust Data, in Scientific Reports, vol. 3, Nature Publishing Group, 2013
- Nicoló Fusi, Christoph Lippert, Karsten Borgwardt, Neil D Lawrence, and Oliver Stegle, Detecting regulatory gene-environment interactions with unmeasured environmental factors, in Bioinformatics, Oxford Univ Press, 2013
- B. Rakitsch, C. Lippert, O. Stegle, and K. Borgwardt, A Lasso Multi-Marker Mixed Model for Association Mapping with Population Structure Correction, in Bioinformatics, Oxford Univ Press, 2013
- Jennifer Listgarten, Christoph Lippert, Eun Yong Kang, Jing Xiang, Carl M. Kadie, and David Heckerman, A powerful and efficient set test for genetic markers that handles confounders, in Bioinformatics, Oxford University Press, 2013
2012
- Christoph Lippert, Gerald Quon, Jennifer Listgarten, and David Heckerman, Extraneous markers used for genetic similarity leads to loss of power in GWAS and heritability determination, no. MSR-TR-2012-120, 3 December 2012
- Jennifer Listgarten, Christoph Lippert, Carl M. Kadie, Robert I. Davidson, Eleazar Eskin, and David Heckerman, Improved linear mixed models for genome-wide association studies, in Nature Methods, vol. 9, no. 6, pp. 3–4, 2012
2011
- C Lippert, J Listgarten, Y Liu, CM Kadie, RI Davidson, and D Heckerman, FaST linear mixed models for genome-wide association studies, in Nature Methods, vol. 8, no. 10, pp. 833–835, October 2011
- O. Stegle, C. Lippert, J. M. Mooij, N. D. Lawrence, and K. Borgwardt, Efficient inference in matrix-variate Gaussian models with iid observation noise, in Advances in Neural Information Processing Systems 24, 2011
- J. Cao, K. Schneeberger, S. Ossowski, T. Günther, S. Bender, J. Fitz, D. Koenig, C. Lanz, O. Stegle, C. Lippert, and others, Whole-genome sequencing of multiple Arabidopsis thaliana populations, in Nature genetics, vol. 43, no. 10, pp. 956–963, Nature Publishing Group, 2011
2010
- C. Lippert, Z. Ghahramani, and K.M. Borgwardt, Gene function prediction from synthetic lethality networks via ranking on demand, in Bioinformatics, vol. 26, no. 7, pp. 912–918, Oxford Univ Press, 2010
2009
- C. Lippert, O. Stegle, Z. Ghahramani, and K.M. Borgwardt, A kernel method for unsupervised structured network inference, in Proceedings of the International Conference on Artificial Intelligence and Statistics (AISTATS), 2009
2008
- C. Lippert, S.H. Weber, Y. Huang, V. Tresp, M. Schubert, and H.P. Kriegel, Relation prediction in multi-relational domains using matrix factorization, in Proceedings of the NIPS 2008 Workshop: Structured Input-Structured Output, Vancouver, Canada, 2008