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 | 121 | 2013 |
LIMIX: genetic analysis of multiple traits C Lippert, FP Casale, B Rakitsch, O Stegle BioRxiv, 003905, 2014 | 107 | 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 | 102 | 2015 |
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 | 75 | 2013 |
Genetic architecture of nonadditive inheritance in Arabidopsis thaliana hybrids DK Seymour, E Chae, DG Grimm, C Martin Pizarro, A Habring-Müller, ... Proceedings of the National Academy of Sciences 113 (46), E7317-E7326, 2016 | 50 | 2016 |
ccSVM: correcting Support Vector Machines for confounding factors in biological data classification L Li, B Rakitsch, K Borgwardt Bioinformatics 27 (13), i342-i348, 2011 | 39 | 2011 |
Translating immunopeptidomics to immunotherapy‐decision‐making for patient and personalized target selection J Fritsche, B Rakitsch, F Hoffgaard, M Römer, H Schuster, DJ Kowalewski, ... Proteomics 18 (12), 1700284, 2018 | 31 | 2018 |
Learning gaussian processes by minimizing pac-bayesian generalization bounds D Reeb, A Doerr, S Gerwinn, B Rakitsch Advances in Neural Information Processing Systems 31, 2018 | 28 | 2018 |
Genomic profiles of diversification and genotype–phenotype association in island nematode lineages A McGaughran, C Rödelsperger, DG Grimm, JM Meyer, E Moreno, ... Molecular biology and evolution 33 (9), 2257-2272, 2016 | 28 | 2016 |
Modelling local gene networks increases power to detect trans-acting genetic effects on gene expression B Rakitsch, O Stegle Genome biology 17, 1-13, 2016 | 26 | 2016 |
Joint genetic analysis using variant sets reveals polygenic gene-context interactions FP Casale, D Horta, B Rakitsch, O Stegle PLoS genetics 13 (4), e1006693, 2017 | 18 | 2017 |
Learning partially known stochastic dynamics with empirical PAC Bayes M Haußmann, S Gerwinn, A Look, B Rakitsch, M Kandemir International Conference on Artificial Intelligence and Statistics, 478-486, 2021 | 13 | 2021 |
LIMIX: genetic analysis of multiple traits. bioRxiv. 2014 C Lippert, FP Casale, B Rakitsch, O Stegle doi 10 (003905), 003905, 0 | 7 | |
Beyond the mean-field: Structured deep Gaussian processes improve the predictive uncertainties J Lindinger, D Reeb, C Lippert, B Rakitsch Advances in Neural Information Processing Systems 33, 8498-8509, 2020 | 5 | 2020 |
LIMIX: genetic analysis of multiple traits. bioRxiv: 003905 C Lippert, FP Casale, B Rakitsch, O Stegle | 4 | 2014 |
Bootstrat: population informed bootstrapping for rare variant tests H Huang, GM Peloso, D Howrigan, B Rakitsch, CJ Simon-Gabriel, ... bioRxiv, 068999, 2016 | 3 | 2016 |
Supplemental Information Multivariate analysis of heritable traits C Lippert, FP Casale, B Rakitsch, O Stegle bioRxiv, 2014 | 3 | 2014 |
Laplace approximated Gaussian process state-space models J Lindinger, B Rakitsch, C Lippert Uncertainty in Artificial Intelligence, 1199-1209, 2022 | 2 | 2022 |
A mixed model approach for joint genetic analysis of alternatively spliced transcript isoforms using RNA-Seq data B Rakitsch, C Lippert, H Topa, K Borgwardt, A Honkela, O Stegle arXiv preprint arXiv:1210.2850, 2012 | 2 | 2012 |
Traversing Time with Multi-Resolution Gaussian Process State-Space Models K Longi, J Lindinger, O Duennbier, M Kandemir, A Klami, B Rakitsch Learning for Dynamics and Control Conference, 366-377, 2022 | 1 | 2022 |