A highly efficient blocked Gibbs sampler reconstruction of multidimensional NMR spectra

Ji Won Yoon; Simon P. Wilson; K. Hun Mok

A highly efficient blocked Gibbs sampler reconstruction of multidimensional NMR spectra

Ji Won Yoon, Simon P. Wilson, K. Hun Mok

Research output: Contribution to journal › Conference article › peer-review

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.

Original language	English
Pages (from-to)	940-947
Number of pages	8
Journal	Journal of Machine Learning Research
Volume	9
Publication status	Published - 2010
Externally published	Yes
Event	13th International Conference on Artificial Intelligence and Statistics, AISTATS 2010 - Sardinia, Italy Duration: 2010 May 13 → 2010 May 15

ASJC Scopus subject areas

Software
Control and Systems Engineering
Statistics and Probability
Artificial Intelligence

Cite this

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title = "A highly efficient blocked Gibbs sampler reconstruction of multidimensional NMR spectra",

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.",

author = "Yoon, {Ji Won} and Wilson, {Simon P.} and Mok, {K. Hun}",

year = "2010",

language = "English",

volume = "9",

pages = "940--947",

journal = "Journal of Machine Learning Research",

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note = "13th International Conference on Artificial Intelligence and Statistics, AISTATS 2010 ; Conference date: 13-05-2010 Through 15-05-2010",

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T1 - A highly efficient blocked Gibbs sampler reconstruction of multidimensional NMR spectra

AU - Yoon, Ji Won

AU - Wilson, Simon P.

AU - Mok, K. Hun

PY - 2010

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N2 - 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.

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.

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M3 - Conference article

AN - SCOPUS:84862284407

SN - 1532-4435

VL - 9

SP - 940

EP - 947

JO - Journal of Machine Learning Research

JF - Journal of Machine Learning Research

T2 - 13th International Conference on Artificial Intelligence and Statistics, AISTATS 2010

Y2 - 13 May 2010 through 15 May 2010

ER -

A highly efficient blocked Gibbs sampler reconstruction of multidimensional NMR spectra

Abstract

ASJC Scopus subject areas

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