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Christoph Lippert
Christoph Lippert
Professor, Hasso Plattner Institute, Universität Potsdam
Bestätigte E-Mail-Adresse bei hpi.de - Startseite
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Zitiert von
Jahr
FaST linear mixed models for genome-wide association studies
C Lippert, J Listgarten, Y Liu, CM Kadie, RI Davidson, D Heckerman
Nature Methods 8 (10), 833-835, 2011
10322011
Whole-genome sequencing of multiple Arabidopsis thaliana populations
J Cao, K Schneeberger, S Ossowski, T Günther, S Bender, J Fitz, ...
Nature genetics 43 (10), 956-963, 2011
10122011
Improved linear mixed models for genome-wide association studies
J Listgarten, C Lippert, CM Kadie, RI Davidson, E Eskin, D Heckerman
Nature methods 9 (6), 525-526, 2012
3422012
Epigenome-wide association studies without the need for cell-type composition
J Zou, C Lippert, D Heckerman, M Aryee, J Listgarten
Nature methods 11 (3), 309-311, 2014
2292014
Identification of individuals by trait prediction using whole-genome sequencing data
C Lippert, R Sabatini, MC Maher, EY Kang, S Lee, O Arikan, A Harley, ...
Proceedings of the National Academy of Sciences 114 (38), 10166-10171, 2017
1422017
A Lasso multi-marker mixed model for association mapping with population structure correction
B Rakitsch, C Lippert, O Stegle, K Borgwardt
Bioinformatics 29 (2), 206-214, 2013
1172013
A genome-to-genome analysis of associations between human genetic variation, HIV-1 sequence diversity, and viral control
I Bartha, JM Carlson, CJ Brumme, PJ McLaren, ZL Brumme, M John, ...
elife 2, e01123, 2013
1152013
Profiling of short-tandem-repeat disease alleles in 12,632 human whole genomes
H Tang, EF Kirkness, C Lippert, WH Biggs, M Fabani, E Guzman, ...
The American Journal of Human Genetics 101 (5), 700-715, 2017
1012017
LIMIX: genetic analysis of multiple traits
C Lippert, FP Casale, B Rakitsch, O Stegle
BioRxiv, 003905, 2014
1002014
Efficient set tests for the genetic analysis of correlated traits
FP Casale, B Rakitsch, C Lippert, O Stegle
Nature methods 12 (8), 755-758, 2015
982015
Efficient inference in matrix-variate Gaussian models with iid observation noise
O Stegle, C Lippert, J Mooij, N Lawrence, K Borgwardt
NIPS 2011: Neural Information Processing Systems, 2011
932011
A powerful and efficient set test for genetic markers that handles confounders
J Listgarten, C Lippert, EY Kang, J Xiang, CM Kadie, D Heckerman
Bioinformatics 29 (12), 1526-1533, 2013
882013
Time to reality check the promises of machine learning-powered precision medicine
J Wilkinson, KF Arnold, EJ Murray, M van Smeden, K Carr, R Sippy, ...
The Lancet Digital Health 2 (12), e677-e680, 2020
862020
FaST-LMM-Select for addressing confounding from spatial structure and rare variants
J Listgarten, C Lippert, D Heckerman
Nature genetics 45 (5), 470-471, 2013
852013
easyGWAS: a cloud-based platform for comparing the results of genome-wide association studies
DG Grimm, D Roqueiro, PA Salomé, S Kleeberger, B Greshake, W Zhu, ...
The Plant Cell 29 (1), 5-19, 2017
822017
Relation prediction in multi-relational domains using matrix factorization
C Lippert, SH Weber, Y Huang, V Tresp, M Schubert, HP Kriegel
NIPS 2008 Workshop: Structured Input-Structured Output 56, 57-60, 2008
822008
An Exhaustive Epistatic SNP Association Analysis on Expanded Wellcome Trust Data
C Lippert, J Listgarten, RI Davidson, S Baxter, H Poong, C Kadie, ...
Scientific Reports 3, 2013
762013
The benefits of selecting phenotype-specific variants for applications of mixed models in genomics
C Lippert, G Quon, EY Kang, CM Kadie, J Listgarten, D Heckerman
Scientific reports 3 (1), 1-9, 2013
702013
It is all in the noise: Efficient multi-task Gaussian process inference with structured residuals
B Rakitsch, C Lippert, K Borgwardt, O Stegle
Advances in neural information processing systems 26, 2013
692013
Further improvements to linear mixed models for genome-wide association studies
C Widmer, C Lippert, O Weissbrod, N Fusi, C Kadie, R Davidson, ...
Scientific reports 4 (1), 1-13, 2014
662014
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