Kaspar Märtens
Title
Cited by
Cited by
Year
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
2832014
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
392016
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
292016
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
222021
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
212016
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
52019
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
52016
Neural Decomposition: Functional ANOVA with Variational Autoencoders
K Märtens, C Yau
International Conference on Artificial Intelligence and Statistics, 2917-2927, 2020
22020
BasisVAE: Translation-invariant feature-level clustering with Variational Autoencoders
K Märtens, C Yau
International Conference on Artificial Intelligence and Statistics, 2928-2937, 2020
22020
Augmented Ensemble MCMC sampling in Factorial Hidden Markov Models
K Märtens, MK Titsias, C Yau
International Conference on Artificial Intelligence and Statistics (AISTATS), 2019
12019
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
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Articles 1–13