Zobrazeno 1 - 10
of 161
pro vyhledávání: '"Hamelryck Thomas"'
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 from variance collapse, i.e. poor predictions due to underestimating u
Externí odkaz:
http://arxiv.org/abs/2410.22948
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 mixtures has used an approximate maximum likelihood estimator for the
Externí odkaz:
http://arxiv.org/abs/2104.13140
Publikováno v:
BMC Bioinformatics, Vol 11, Iss 1, p 429 (2010)
Abstract Background Genome sequencing projects have expanded the gap between the amount of known protein sequences and structures. The limitations of current high resolution structure determination methods make it unlikely that this gap will disappea
Externí odkaz:
https://doaj.org/article/2d17eea1e85c4fe4a2b133bf40456f1d
Autor:
Frellsen Jes, Paluszewski Martin, Boomsma Wouter, Harder Tim, Johansson Kristoffer E, Hamelryck Thomas
Publikováno v:
BMC Bioinformatics, Vol 11, Iss 1, p 306 (2010)
Abstract Background Accurately covering the conformational space of amino acid side chains is essential for important applications such as protein design, docking and high resolution structure prediction. Today, the most common way to capture this co
Externí odkaz:
https://doaj.org/article/7c75cb6bcb9c419f97cf04ddcc83d9c3
Autor:
Hamelryck Thomas, Paluszewski Martin
Publikováno v:
BMC Bioinformatics, Vol 11, Iss 1, p 126 (2010)
Abstract Background Mocapy++ is a toolkit for parameter learning and inference in dynamic Bayesian networks (DBNs). It supports a wide range of DBN architectures and probability distributions, including distributions from directional statistics (the
Externí odkaz:
https://doaj.org/article/2b31e6d4d32d4fccb7561745f471badf
Publikováno v:
BMC Bioinformatics, Vol 8, Iss 1, p 357 (2007)
Abstract Background The prediction of the secondary structure of proteins is one of the most studied problems in bioinformatics. Despite their success in many problems of biological sequence analysis, Hidden Markov Models (HMMs) have not been used mu
Externí odkaz:
https://doaj.org/article/fdb50160b3e84c1293d0c8476414c4e2
Publikováno v:
Algorithms for Molecular Biology, Vol 1, Iss 1, p 20 (2006)
Abstract Background A new, promising solvent exposure measure, called half-sphere-exposure (HSE), has recently been proposed. Here, we study the reconstruction of a protein's Cα trace solely from structure-derived HSE information. This problem is of
Externí odkaz:
https://doaj.org/article/9211ad7d83124d1b86f8aba32e9f4245
Autor:
Baranov Pavel V, Vestergaard Bente, Hamelryck Thomas, Gesteland Raymond F, Nyborg Jens, Atkins John F
Publikováno v:
Biology Direct, Vol 1, Iss 1, p 28 (2006)
Abstract Background While all codons that specify amino acids are universally recognized by tRNA molecules, codons signaling termination of translation are recognized by proteins known as class-I release factors (RF). In most eukaryotes and archaea a
Externí odkaz:
https://doaj.org/article/e04726b95262470eb36e8854206f6c19
Autor:
Hamelryck Thomas, Boomsma Wouter
Publikováno v:
BMC Bioinformatics, Vol 6, Iss 1, p 159 (2005)
Abstract Background Various forms of the so-called loop closure problem are crucial to protein structure prediction methods. Given an N- and a C-terminal end, the problem consists of finding a suitable segment of a certain length that bridges the end
Externí odkaz:
https://doaj.org/article/b97b9f1dd4eb4e0dba00234a71a040a6
Autor:
García-Portugués, Eduardo, Golden, Michael, Sørensen, Michael, Mardia, Kanti V., Hamelryck, Thomas, Hein, Jotun
Publikováno v:
In Ley, C. and Verdebout, T., editors, Applied Directional Statistics, pages 61-90. CRC Press, 2018
This chapter shows how toroidal diffusions are convenient methodological tools for modelling protein evolution in a probabilistic framework. The chapter addresses the construction of ergodic diffusions with stationary distributions equal to well-know
Externí odkaz:
http://arxiv.org/abs/1804.00285