78 citations · 215 across the 10 of their papers we have counts for
10 papers
Compact Circulant Layers with Spectral Priors
Joseph Margaryan, Thomas Hamelryck
Critical applications in areas such as medicine, robotics and autonomous systems require compact (i.e., memory efficient), uncertainty-aware neural networks suitable for edge and o…
AlphaFold's Bayesian Roots in Probability Kinematics
Thomas Hamelryck, Kanti V. Mardia
The seminal breakthrough of AlphaFold in protein structure prediction relied on a learned potential energy function parameterized by deep models, in contrast to its successors Alph…
ELBOing Stein: Variational Bayes with Stein Mixture Inference
Ola Rønning, Eric Nalisnick, Christophe Ley +2
Stein variational gradient descent (SVGD) [Liu and Wang, 2016] performs approximate Bayesian inference by representing the posterior with a set of particles. However, SVGD suffers…
Mixture models for spherical data with applications to protein bioinformatics
Kanti V. Mardia, Stuart Barber, Philippa M. Burdett +2
Finite mixture models are fitted to spherical data. Kent distributions are used for the components of the mixture because they allow considerable flexibility. Previous work on such…
Persistent topology of protein space
W. Hamilton, J. E. Borgert, T. Hamelryck +1
Protein fold classification is a classic problem in structural biology and bioinformatics. We approach this problem using persistent homology. In particular, we use alpha shape fil…
Toroidal diffusions and protein structure evolution
Eduardo García-Portugués, Michael Golden, Michael Sørensen +3
This chapter shows how toroidal diffusions are convenient methodological tools for modelling protein evolution in a probabilistic framework. The chapter addresses the construction…
Langevin diffusions on the torus: estimation and applications
Eduardo García-Portugués, Michael Sørensen, Kanti V. Mardia +1
We introduce stochastic models for continuous-time evolution of angles and develop their estimation. We focus on studying Langevin diffusions with stationary distributions equal to…
A generative angular model of protein structure evolution
Michael Golden, Eduardo García-Portugués, Michael Sørensen +3
Recently described stochastic models of protein evolution have demonstrated that the inclusion of structural information in addition to amino acid sequences leads to a more reliabl…
Protein structure validation and refinement using amide proton chemical shifts derived from quantum mechanics
Anders S. Christensen, Troels E. Linnet, Mikael Borg +4
We present the ProCS method for the rapid and accurate prediction of protein backbone amide proton chemical shifts - sensitive probes of the geometry of key hydrogen bonds that det…
Potentials of Mean Force for Protein Structure Prediction Vindicated, Formalized and Generalized
Thomas Hamelryck, Mikael Borg, Martin Paluszewski +6
Understanding protein structure is of crucial importance in science, medicine and biotechnology. For about two decades, knowledge based potentials based on pairwise distances -- so…