Bayesian protein superposition using Hamiltonian Monte Carlo

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Bayesian protein superposition using Hamiltonian Monte Carlo. / Moreta, Lys Sanz; Al-Sibahi, Ahmad Salim; Hamelryck, Thomas.

Proceedings - IEEE 20th International Conference on Bioinformatics and Bioengineering, BIBE 2020. IEEE, 2020. p. 1-11 9288019.

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

Harvard

Moreta, LS, Al-Sibahi, AS & Hamelryck, T 2020, Bayesian protein superposition using Hamiltonian Monte Carlo. in Proceedings - IEEE 20th International Conference on Bioinformatics and Bioengineering, BIBE 2020., 9288019, IEEE, pp. 1-11, 20th IEEE International Conference on Bioinformatics and Bioengineering, BIBE 2020, Virtual, Cincinnati, United States, 26/10/2020. https://doi.org/10.1109/BIBE50027.2020.00009

APA

Moreta, L. S., Al-Sibahi, A. S., & Hamelryck, T. (2020). Bayesian protein superposition using Hamiltonian Monte Carlo. In Proceedings - IEEE 20th International Conference on Bioinformatics and Bioengineering, BIBE 2020 (pp. 1-11). [9288019] IEEE. https://doi.org/10.1109/BIBE50027.2020.00009

Vancouver

Moreta LS, Al-Sibahi AS, Hamelryck T. Bayesian protein superposition using Hamiltonian Monte Carlo. In Proceedings - IEEE 20th International Conference on Bioinformatics and Bioengineering, BIBE 2020. IEEE. 2020. p. 1-11. 9288019 https://doi.org/10.1109/BIBE50027.2020.00009

Author

Moreta, Lys Sanz ; Al-Sibahi, Ahmad Salim ; Hamelryck, Thomas. / Bayesian protein superposition using Hamiltonian Monte Carlo. Proceedings - IEEE 20th International Conference on Bioinformatics and Bioengineering, BIBE 2020. IEEE, 2020. pp. 1-11

Bibtex

@inproceedings{2ca4a7756da9458cba81280577c64cc7,
title = "Bayesian protein superposition using Hamiltonian Monte Carlo",
abstract = "Optimally superimposing protein structures is essential to study their structure, function, dynamics and evolution. We present THESEUS NUTS (No U-Turn Sampler), a Bayesian version of the THESEUS model [1] -[3] which relies on maximum likelihood estimation. The probabilistic model interprets each protein as a rotated and translated noisy observation of a latent mean structure. Unlike conventional methods [4], THESEUS takes into account the differences in correlations between the atoms in the structure. This paper extends the previous THESEUS MAP (Maximum A Posteriori) model, [5] to full Bayesian inference by making use of the iterative NUTS [6], a Hamiltonian Monte Carlo method. The model delivers consistent results and is computationally efficient thanks to its implementation in the probabilistic programming language NumpPyro [7], [8] which in turn relies upon JAX [9], a system for high-performance machine learning.",
keywords = "Bayesian modelling, Hamiltonian Monte Carlo, NUTS, probabilistic programming, protein structure superposition, protein superposition",
author = "Moreta, {Lys Sanz} and Al-Sibahi, {Ahmad Salim} and Thomas Hamelryck",
year = "2020",
month = oct,
doi = "10.1109/BIBE50027.2020.00009",
language = "English",
pages = "1--11",
booktitle = "Proceedings - IEEE 20th International Conference on Bioinformatics and Bioengineering, BIBE 2020",
publisher = "IEEE",
note = "20th IEEE International Conference on Bioinformatics and Bioengineering, BIBE 2020 ; Conference date: 26-10-2020 Through 28-10-2020",

}

RIS

TY - GEN

T1 - Bayesian protein superposition using Hamiltonian Monte Carlo

AU - Moreta, Lys Sanz

AU - Al-Sibahi, Ahmad Salim

AU - Hamelryck, Thomas

PY - 2020/10

Y1 - 2020/10

N2 - Optimally superimposing protein structures is essential to study their structure, function, dynamics and evolution. We present THESEUS NUTS (No U-Turn Sampler), a Bayesian version of the THESEUS model [1] -[3] which relies on maximum likelihood estimation. The probabilistic model interprets each protein as a rotated and translated noisy observation of a latent mean structure. Unlike conventional methods [4], THESEUS takes into account the differences in correlations between the atoms in the structure. This paper extends the previous THESEUS MAP (Maximum A Posteriori) model, [5] to full Bayesian inference by making use of the iterative NUTS [6], a Hamiltonian Monte Carlo method. The model delivers consistent results and is computationally efficient thanks to its implementation in the probabilistic programming language NumpPyro [7], [8] which in turn relies upon JAX [9], a system for high-performance machine learning.

AB - Optimally superimposing protein structures is essential to study their structure, function, dynamics and evolution. We present THESEUS NUTS (No U-Turn Sampler), a Bayesian version of the THESEUS model [1] -[3] which relies on maximum likelihood estimation. The probabilistic model interprets each protein as a rotated and translated noisy observation of a latent mean structure. Unlike conventional methods [4], THESEUS takes into account the differences in correlations between the atoms in the structure. This paper extends the previous THESEUS MAP (Maximum A Posteriori) model, [5] to full Bayesian inference by making use of the iterative NUTS [6], a Hamiltonian Monte Carlo method. The model delivers consistent results and is computationally efficient thanks to its implementation in the probabilistic programming language NumpPyro [7], [8] which in turn relies upon JAX [9], a system for high-performance machine learning.

KW - Bayesian modelling

KW - Hamiltonian Monte Carlo

KW - NUTS

KW - probabilistic programming

KW - protein structure superposition

KW - protein superposition

UR - http://www.scopus.com/inward/record.url?scp=85099556496&partnerID=8YFLogxK

U2 - 10.1109/BIBE50027.2020.00009

DO - 10.1109/BIBE50027.2020.00009

M3 - Article in proceedings

AN - SCOPUS:85099556496

SP - 1

EP - 11

BT - Proceedings - IEEE 20th International Conference on Bioinformatics and Bioengineering, BIBE 2020

PB - IEEE

T2 - 20th IEEE International Conference on Bioinformatics and Bioengineering, BIBE 2020

Y2 - 26 October 2020 through 28 October 2020

ER -

ID: 255836764