| DNA methylome profiling of human tissues identifies global and tissue-specific methylation patterns K Lokk, V Modhukur, B Rajashekar, K Märtens, R Mägi, R Kolde, ... Genome biology 15 (4), 3248, 2014 | 186 | 2014 |
| Predicting quantitative traits from genome and phenome with near perfect accuracy K Märtens, J Hallin, J Warringer, G Liti, L Parts Nature communications 7, 11512, 2016 | 19 | 2016 |
| Powerful decomposition of complex traits in a diploid model J Hallin, K Märtens, AI Young, M Zackrisson, F Salinas, L Parts, ... Nature communications 7, 13311, 2016 | 18* | 2016 |
| seqlm: an MDL based method for identifying differentially methylated regions in high density methylation array data R Kolde, K Märtens, K Lokk, S Laur, J Vilo Bioinformatics 32 (17), 2604-2610, 2016 | 11 | 2016 |
| Erratum to: DNA methylome profiling of human tissues identifies global and tissue-specific methylation patterns K Lokk, V Modhukur, B Rajashekar, K Märtens, R Mägi, R Kolde, ... Genome biology 17 (1), 224, 2016 | 2 | 2016 |
| Decomposing feature-level variation with Covariate Gaussian Process Latent Variable Models K Martens, K Campbell, C Yau International Conference on Machine Learning, 4372-4381, 2019 | | 2019 |
| Augmented Ensemble MCMC sampling in Factorial Hidden Markov Models K Märtens, MK Titsias, C Yau International Conference on Artificial Intelligence and Statistics (AISTATS), 2019 | | 2019 |
| Covariate Gaussian Process Latent Variable Models K Märtens, KR Campbell, C Yau arXiv preprint arXiv:1810.06983, 2018 | | 2018 |
Jaak ViloProf. of Bioinformatics, Institute of Computer Science, University of Tartu, EstoniaVerified email at ut.ee