A highly efficient blocked Gibbs sampler reconstruction of multidimensional NMR spectra

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

Research output: Contribution to journalArticle

2 Citations (Scopus)

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 languageEnglish
Pages (from-to)940-947
Number of pages8
JournalJournal of Machine Learning Research
Volume9
Publication statusPublished - 2010 Dec 1
Externally publishedYes

Fingerprint

Gibbs Sampler
Nuclear magnetic resonance
Projection
Nuclear Magnetic Resonance
Statistical method
Statistical methods
Efficient Algorithms
Protein
Three-dimensional

ASJC Scopus subject areas

  • Artificial Intelligence
  • Software
  • Control and Systems Engineering
  • Statistics and Probability

Cite this

A highly efficient blocked Gibbs sampler reconstruction of multidimensional NMR spectra. / Yoon, Ji Won; Wilson, Simon P.; Mok, K. Hun.

In: Journal of Machine Learning Research, Vol. 9, 01.12.2010, p. 940-947.

Research output: Contribution to journalArticle

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