bayesian peak picking for nmr spectra

Clicks: 254
ID: 216801
2014
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Abstract
Protein structure determination is a very important topic in structural genomics, which helps people to understand varieties of biological functions such as protein-protein interactions, protein–DNA interactions and so on. Nowadays, nuclear magnetic resonance (NMR) has often been used to determine the three-dimensional structures of protein in vivo. This study aims to automate the peak picking step, the most important and tricky step in NMR structure determination. We propose to model the NMR spectrum by a mixture of bivariate Gaussian densities and use the stochastic approximation Monte Carlo algorithm as the computational tool to solve the problem. Under the Bayesian framework, the peak picking problem is casted as a variable selection problem. The proposed method can automatically distinguish true peaks from false ones without preprocessing the data. To the best of our knowledge, this is the first effort in the literature that tackles the peak picking problem for NMR spectrum data using Bayesian method.
Reference Key
cheng2014genomics,bayesian Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;Yichen Cheng;Xin Gao;Faming Liang
Journal journal of ethics
Year 2014
DOI
10.1016/j.gpb.2013.07.003
URL
http://www.sciencedirect.com/science/article/pii/S1672022913001162
Keywords
nuclear magnetic resonance markov chain monte carlo

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