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 language | English |
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Pages (from-to) | 89-99 |
Number of pages | 11 |
Journal | Computers in Biology and Medicine |
Volume | 54 |
DOIs | |
Publication status | Published - 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