Christoph Lippert

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David HeckermanMicrosoft ResearchVerified email at microsoft.com
Oliver StegleGerman Cancer Research Center (DKFZ) & EMBL HeidelbergVerified email at dkfz.de
Jennifer ListgartenProfessor, UC Berkeley EECS and Center for Computational BiologyVerified email at berkeley.edu
Karsten BorgwardtDirector, Max Planck Institute of BiochemistryVerified email at biochem.mpg.de
Carl KadieMicrosoft Research (retired)Verified email at benkadie.com
Barbara RakitschBosch Center for AIVerified email at de.bosch.com
Detlef WeigelMax Planck Institute for Biology TübingenVerified email at weigelworld.org
Stefan KonigorskiSenior Researcher, Hasso Plattner InstituteVerified email at hpi.de
Benjamin BergnerPhD candidate, Hasso Plattner InstituteVerified email at hpi.de
Aiham TalebMachine Learning & Digital Health, Hasso Plattner Institute, University of PotsdamVerified email at hpi.de
Neil D. LawrenceUniversity of CambridgeVerified email at cl.cam.ac.uk
Nicolo FusiPartner Researcher, Microsoft ResearchVerified email at microsoft.com
Eun Yong KangMachine Learning Scientist, MicrosoftVerified email at microsoft.com
Marius KloftTU KaiserslauternVerified email at cs.uni-kl.de
Jun CaoCorteva AgriscienceVerified email at corteva.com
Karl SchmidProfessor of Crop Biodiversity and Breeding Informatics, University of Hohenheim, Stuttgart, GermanyVerified email at uni-hohenheim.de
Korbinian SchneebergerLudwig-Maximilians-University Munich (LMU); Max Planck Institute for Plant Breeding ResearchVerified email at lmu.de
Torsten GüntherResearcher, Evolutionary Biology Centre, Uppsala UniversityVerified email at ebc.uu.se
Daniel KoenigUniversity of California, RiversideVerified email at ucr.edu
Stephan OssowskiInstitute of Medical Genetics and Applied Genomics, University of Tübingen, Tübingen, GermanyVerified email at crg.eu

Christoph Lippert

Professor, Hasso Plattner Institute, Universität Potsdam

Verified email at hpi.de - Homepage

Statistical Genetics Computational Biology Machine Learning Bioinformatics Digital Health


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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	1136	2011
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	1046	2011
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	357	2012
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	243	2014
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	178	2017
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	137	2020
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	130	2017
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	128	2013
3d self-supervised methods for medical imaging A Taleb, W Loetzsch, N Danz, J Severin, T Gaertner, B Bergner, C Lippert Advances in neural information processing systems 33, 18158-18172, 2020	125	2020
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	125	2013
LIMIX: genetic analysis of multiple traits C Lippert, FP Casale, B Rakitsch, O Stegle BioRxiv, 003905, 2014	110	2014
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	107	2015
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	101	2011
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	99	2017
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	91	2013
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	88	2013
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	85	2008
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	82	2013
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	81	2014
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	77	2013

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