A highly efficient blocked Gibbs sampler reconstruction of multidimensional NMR spectra

Ji Won Yoon, Simon Wilson, K. Hun Mok
Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, PMLR 9:940-947, 2010.

Abstract

Projection Reconstruction Nuclear Magnetic Resonance (PR-NMR) is a new technique to generate multi-dimensional NMR spectra, which have discrete features that are relatively sparsely distributed in space. A small number of projections from lower dimensional NMR spectra are used to reconstruct the multi-dimensional NMR spectra. We propose an efficient algorithm which employs a blocked Gibbs sampler to accurately reconstruct NMR spectra. This statistical method generates samples in Bayesian scheme. Our proposed algorithm is tested on a set of six projections derived from the three-dimensional 700 MHz HNCO spectrum of HasA, a 187-residue heme binding protein.

Cite this Paper

BibTeX
@InProceedings{pmlr-v9-yoon10a, title = {A highly efficient blocked Gibbs sampler reconstruction of multidimensional NMR spectra}, author = {Yoon, Ji Won and Wilson, Simon and Mok, K. Hun}, booktitle = {Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics}, pages = {940--947}, year = {2010}, editor = {Teh, Yee Whye and Titterington, Mike}, volume = {9}, series = {Proceedings of Machine Learning Research}, address = {Chia Laguna Resort, Sardinia, Italy}, month = {13--15 May}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v9/yoon10a/yoon10a.pdf}, url = {https://proceedings.mlr.press/v9/yoon10a.html}, abstract = {Projection Reconstruction Nuclear Magnetic Resonance (PR-NMR) is a new technique to generate multi-dimensional NMR spectra, which have discrete features that are relatively sparsely distributed in space. A small number of projections from lower dimensional NMR spectra are used to reconstruct the multi-dimensional NMR spectra. We propose an efficient algorithm which employs a blocked Gibbs sampler to accurately reconstruct NMR spectra. This statistical method generates samples in Bayesian scheme. Our proposed algorithm is tested on a set of six projections derived from the three-dimensional 700 MHz HNCO spectrum of HasA, a 187-residue heme binding protein.} }
Endnote
%0 Conference Paper %T A highly efficient blocked Gibbs sampler reconstruction of multidimensional NMR spectra %A Ji Won Yoon %A Simon Wilson %A K. Hun Mok %B Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2010 %E Yee Whye Teh %E Mike Titterington %F pmlr-v9-yoon10a %I PMLR %P 940--947 %U https://proceedings.mlr.press/v9/yoon10a.html %V 9 %X Projection Reconstruction Nuclear Magnetic Resonance (PR-NMR) is a new technique to generate multi-dimensional NMR spectra, which have discrete features that are relatively sparsely distributed in space. A small number of projections from lower dimensional NMR spectra are used to reconstruct the multi-dimensional NMR spectra. We propose an efficient algorithm which employs a blocked Gibbs sampler to accurately reconstruct NMR spectra. This statistical method generates samples in Bayesian scheme. Our proposed algorithm is tested on a set of six projections derived from the three-dimensional 700 MHz HNCO spectrum of HasA, a 187-residue heme binding protein.
RIS
TY - CPAPER TI - A highly efficient blocked Gibbs sampler reconstruction of multidimensional NMR spectra AU - Ji Won Yoon AU - Simon Wilson AU - K. Hun Mok BT - Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics DA - 2010/03/31 ED - Yee Whye Teh ED - Mike Titterington ID - pmlr-v9-yoon10a PB - PMLR DP - Proceedings of Machine Learning Research VL - 9 SP - 940 EP - 947 L1 - http://proceedings.mlr.press/v9/yoon10a/yoon10a.pdf UR - https://proceedings.mlr.press/v9/yoon10a.html AB - Projection Reconstruction Nuclear Magnetic Resonance (PR-NMR) is a new technique to generate multi-dimensional NMR spectra, which have discrete features that are relatively sparsely distributed in space. A small number of projections from lower dimensional NMR spectra are used to reconstruct the multi-dimensional NMR spectra. We propose an efficient algorithm which employs a blocked Gibbs sampler to accurately reconstruct NMR spectra. This statistical method generates samples in Bayesian scheme. Our proposed algorithm is tested on a set of six projections derived from the three-dimensional 700 MHz HNCO spectrum of HasA, a 187-residue heme binding protein. ER -
APA
Yoon, J.W., Wilson, S. & Mok, K.H.. (2010). A highly efficient blocked Gibbs sampler reconstruction of multidimensional NMR spectra. Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 9:940-947 Available from https://proceedings.mlr.press/v9/yoon10a.html.

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