Jes Frellsen
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A probabilistic model of RNA conformational space
J Frellsen, I Moltke, M Thiim, KV Mardia, J Ferkinghoff-Borg, T Hamelryck
PLoS computational biology 5 (6), e1000406, 2009
Potentials of mean force for protein structure prediction vindicated, formalized and generalized
T Hamelryck, M Borg, M Paluszewski, J Paulsen, J Frellsen, C Andreetta, ...
PloS one 5 (11), e13714, 2010
MIWAE: Deep Generative Modelling and Imputation of Incomplete Data Sets
PA Mattei, J Frellsen
Proceedings of the 36th International Conference on Machine Learning, PMLR …, 2019
Beyond rotamers: a generative, probabilistic model of side chains in proteins
T Harder, W Boomsma, M Paluszewski, J Frellsen, KE Johansson, ...
BMC bioinformatics 11 (1), 1-13, 2010
Spherical convolutions and their application in molecular modelling.
W Boomsma, J Frellsen
Advances in Neural Information Processing Systems 30 (NeurIPS 2017) 2, 6, 2017
Adaptable probabilistic mapping of short reads using position specific scoring matrices
P Kerpedjiev, J Frellsen, S Lindgreen, A Krogh
BMC bioinformatics 15 (1), 1-17, 2014
Inference of structure ensembles of flexible biomolecules from sparse, averaged data
S Olsson, J Frellsen, W Boomsma, KV Mardia, T Hamelryck
PloS one 8 (11), e79439, 2013
Asap: a framework for over-representation statistics for transcription factor binding sites
TT Marstrand, J Frellsen, I Moltke, M Thiim, E Valen, D Retelska, A Krogh
PLoS One 3 (2), e1623, 2008
PHAISTOS: a framework for Markov chain Monte Carlo simulation and inference of protein structure
W Boomsma, J Frellsen, T Harder, S Bottaro, KE Johansson, P Tian, ...
Journal of computational chemistry 34 (19), 1697-1705, 2013
Leveraging the exact likelihood of deep latent variable models
PA Mattei, J Frellsen
Advances in Neural Information Processing Systems 31 (NeurIPS 2018), 2018
Equilibrium simulations of proteins using molecular fragment replacement and NMR chemical shifts
W Boomsma, P Tian, J Frellsen, J Ferkinghoff-Borg, T Hamelryck, ...
Proceedings of the National Academy of Sciences 111 (38), 13852-13857, 2014
Generative probabilistic models extend the scope of inferential structure determination
S Olsson, W Boomsma, J Frellsen, S Bottaro, T Harder, J Ferkinghoff-Borg, ...
Journal of Magnetic Resonance 213 (1), 182-186, 2011
The Multivariate Generalised von Mises Distribution: Inference and Applications
AKW Navarro, J Frellsen, RE Turner
Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence …, 2017
Partially Exchangeable Networks and Architectures for Learning Summary Statistics in Approximate Bayesian Computation
S Wiqvist, M Pierre-Alexandre, U Picchini, J Frellsen
Proceedings of the 36th International Conference on Machine Learning, PMLR …, 2019
Statistics of Bivariate von Mises Distributions
KV Mardia, J Frellsen
Bayesian Methods in Structural Bioinformatics, 159-178, 2012
Comparative study of inference methods for Bayesian nonnegative matrix factorisation
T Brouwer, J Frellsen, P Lió
Joint European Conference on Machine Learning and Knowledge Discovery in …, 2017
Formulation of probabilistic models of protein structure in atomic detail using the reference ratio method
JB Valentin, C Andreetta, W Boomsma, S Bottaro, J Ferkinghoff‐Borg, ...
Proteins: Structure, Function, and Bioinformatics 82 (2), 288-299, 2014
Bayesian generalised ensemble markov chain monte carlo
J Frellsen, O Winther, Z Ghahramani, J Ferkinghoff-Borg
Artificial Intelligence and Statistics, 408-416, 2016
Towards a General Probabilistic Model of Protein Structure: The Reference Ratio Method
J Frellsen, KV Mardia, M Borg, J Ferkinghoff-Borg, T Hamelryck
Bayesian Methods in Structural Bioinformatics, 125-134, 2012
On the accuracy of short read mapping
P Menzel, J Frellsen, M Plass, SH Rasmussen, A Krogh
Deep Sequencing Data Analysis, 39-59, 2013
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