title: Bayesian reconstruction of projection reconstruction NMR (PR-NMR).
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, 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.
authors.affiliation: Cyber Defense Department and Graduate School of Information Security, Korea University, South Korea. Electronic address: jiwon_yoon@korea.ac.kr.
authors.forename: Ji Won
authors.initials: JW
authors.lastname: Yoon
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D009682:Magnetic Resonance Spectroscopy / Q000379:methods*; D008390:Markov Chains; D008956:Models, Chemical; D015233:Models, Statistical; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity
keywords: Bayesian model selection; Inverse problem; Mixed linear model; Projection reconstruction; Reconstruction of multidimensional NMR spectra