| 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, 2021 | 773 | 2021 |
| 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, 1-14, 2014 | 423 | 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), 11512, 2016 | 46 | 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), 13311, 2016 | 40 | 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 | 34 | 2016 |
| BasisVAE: Translation-invariant feature-level clustering with Variational Autoencoders K Märtens, C Yau International Conference on Artificial Intelligence and Statistics, 2928-2937, 2020 | 14 | 2020 |
| 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 | 13 | 2019 |
| Neural decomposition: Functional anova with variational autoencoders K Märtens, C Yau International Conference on Artificial Intelligence and Statistics, 2917-2927, 2020 | 9 | 2020 |
| 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, 2016 | 8 | 2016 |
| 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 47 (3), 360-372, 2021 | 5 | 2021 |
| 629 Salumets A, and Tonisson N K Lokk, V Modhukur, B Rajashekar, K Martens, R Magi, R Kolde, ... DNA methylome profiling of human tissues identifies global and 630, 2014 | 5 | 2014 |
| Augmented Ensemble MCMC sampling in Factorial Hidden Markov Models K Märtens, MK Titsias, C Yau International Conference on Artificial Intelligence and Statistics (AISTATS), 2019 | 4 | 2019 |
| Rarity: discovering rare cell populations from single-cell imaging data K Märtens, M Bortolomeazzi, L Montorsi, J Spencer, F Ciccarelli, C Yau Bioinformatics 39 (12), btad750, 2023 | 2 | 2023 |
| Enabling feature-level interpretability in non-linear latent variable models: a synthesis of statistical and machine learning techniques K Martens University of Oxford, 2019 | 1 | 2019 |
| Disentangling shared and private latent factors in multimodal Variational Autoencoders K Märtens, C Yau Machine Learning in Computational Biology, 60-75, 2024 | | 2024 |
| Deep stochastic processes via functional Markov transition operators J Xu, E Dupont, K Märtens, T Rainforth, YW Teh Advances in Neural Information Processing Systems 36, 2024 | | 2024 |
| DNA methylome profiling of human tissues identifies global and tissue-specific methylation patterns (vol 15, r54, 2014) K Lokk, V Modhukur, B Rajashekar, K Martens, R Magi, R Kolde, ... GENOME BIOLOGY 17, 2016 | | 2016 |
| Enhancing generative perturbation models with LLM-informed gene embeddings K Märtens, R Donovan-Maiye, J Ferkinghoff-Borg ICLR 2024 Workshop on Machine Learning for Genomics Explorations, 0 | | |
Christopher YauUniversity of OxfordVerified email at wrh.ox.ac.uk
