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 |
|---|---|
| Pages (from-to) | 89-99 |
| Number of pages | 11 |
| Journal | Computers in Biology and Medicine |
| Volume | 54 |
| DOIs | |
| Publication status | Published - 2014 Nov 1 |
Bibliographical note
Funding Information:This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT and Future Planning (NRF- 2013R1A1A1012797 ). The author is also supported by new faculty research grant from Korea University .
Publisher Copyright:
© 2014 Elsevier Ltd.
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