کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
504908 | 864450 | 2014 | 11 صفحه PDF | دانلود رایگان |
• We design a mathematical model for reconstructing multidimensional NMR spectra.
• We compare the performance of several statistical algorithms for PR-NMR.
• A generalized Monte Carlo algorithm is applied to restore the NMR spectra.
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] and [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.
Journal: Computers in Biology and Medicine - Volume 54, 1 November 2014, Pages 89–99