Bayesian reconstruction of projection reconstruction NMR (PR-NMR)

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Abstract

Projection reconstruction nuclear magnetic resonance (PR-NMR) is a technique for generating multidimensional NMR spectra. A small number of projections from lower-dimensional NMR spectra are used to reconstruct the multidimensional NMR spectra. In our previous work [1,2], it was shown that multidimensional NMR spectra are efficiently reconstructed using peak-by-peak based reversible jump Markov chain Monte Carlo (RJMCMC) algorithm. We propose an extended and generalized RJMCMC algorithm replacing a simple linear model with a linear mixed model to reconstruct close NMR spectra into true spectra. This statistical method generates samples in a Bayesian scheme. Our proposed algorithm is tested on a set of six projections derived from the three-dimensional 700. MHz HNCO spectrum of a protein HasA.

Original languageEnglish
Pages (from-to)89-99
Number of pages11
JournalComputers in Biology and Medicine
Volume54
DOIs
Publication statusPublished - 2014 Nov 1

Keywords

  • Bayesian model selection
  • Inverse problem
  • Mixed linear model
  • Projection reconstruction
  • Reconstruction of multidimensional nmr spectra

ASJC Scopus subject areas

  • Computer Science Applications
  • Health Informatics

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