| 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 | 1336 | 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 | 1103 | 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 | 394 | 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 | 260 | 2014 |
| 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 | 249 | 2020 |
| 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 | 222 | 2020 |
| 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 | 208 | 2017 |
| 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 | 168 | 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 | 148 | 2013 |
| Multimodal self-supervised learning for medical image analysis A Taleb, C Lippert, T Klein, M Nabi International conference on information processing in medical imaging, 661-673, 2021 | 144 | 2021 |
| 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 | 137 | 2013 |
| Pathogenic variants damage cell composition and single cell transcription in cardiomyopathies D Reichart, EL Lindberg, H Maatz, AMA Miranda, A Viveiros, N Shvetsov, ... Science 377 (6606), eabo1984, 2022 | 130 | 2022 |
| LIMIX: genetic analysis of multiple traits C Lippert, FP Casale, B Rakitsch, O Stegle BioRxiv, 003905, 2014 | 122 | 2014 |
| 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 | 121 | 2017 |
| 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 | 120 | 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 | 111 | 2011 |
| 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 | 97 | 2013 |
| 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 | 94 | 2013 |
| 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 | 91 | 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), 6874, 2014 | 89 | 2014 |
David HeckermanMicrosoft Research在 microsoft.com 的电子邮件经过验证
