| 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), 1-14, 2014 | 283 | 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 (1), 1-8, 2016 | 39 | 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 (1), 1-9, 2016 | 29 | 2016 |
| Bayesian statistics and modelling R van de Schoot, S Depaoli, R King, B Kramer, K Märtens, MG Tadesse, ... Nature Reviews Methods Primers 1 (1), 1-26, 2021 | 22 | 2021 |
| 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 | 21 | 2016 |
| Decomposing feature-level variation with Covariate Gaussian Process Latent Variable Models K Märtens, KR Campbell, C Yau Proceedings of the 36th International Conference on Machine Learning (ICML), 2019 | 5 | 2019 |
| 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), 1-1, 2016 | 5 | 2016 |
| Neural Decomposition: Functional ANOVA with Variational Autoencoders K Märtens, C Yau International Conference on Artificial Intelligence and Statistics, 2917-2927, 2020 | 2 | 2020 |
| BasisVAE: Translation-invariant feature-level clustering with Variational Autoencoders K Märtens, C Yau International Conference on Artificial Intelligence and Statistics, 2928-2937, 2020 | 2 | 2020 |
| Augmented Ensemble MCMC sampling in Factorial Hidden Markov Models K Märtens, MK Titsias, C Yau International Conference on Artificial Intelligence and Statistics (AISTATS), 2019 | 1 | 2019 |
| Associations between baseline opioid use disorder severity, mental health and biopsychosocial functioning, with clinical responses to computer-assisted therapy treatment S Elison-Davies, K Märtens, C Yau, G Davies, J Ward The American Journal of Drug and Alcohol Abuse, 1-13, 2021 | | 2021 |
| Enabling feature-level interpretability in non-linear latent variable models: a synthesis of statistical and machine learning techniques K Martens University of Oxford, 2019 | | 2019 |
| Rejection-free Ensemble MCMC with applications to Factorial Hidden Markov Models K Märtens, MK Titsias, C Yau | | |
Jaak ViloProf. of Bioinformatics, Institute of Computer Science, University of Tartu, EstoniaVerified email at ut.ee
