TY - JOUR
T1 - Bayesian Peak Picking for NMR Spectra
AU - Cheng, Yichen
AU - Gao, Xin
AU - Liang, Faming
N1 - KAUST Repository Item: Exported on 2020-10-01
PY - 2014/2
Y1 - 2014/2
N2 - 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.
AB - 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.
UR - http://hdl.handle.net/10754/552357
UR - http://linkinghub.elsevier.com/retrieve/pii/S1672022913001162
UR - http://www.scopus.com/inward/record.url?scp=84894269593&partnerID=8YFLogxK
U2 - 10.1016/j.gpb.2013.07.003
DO - 10.1016/j.gpb.2013.07.003
M3 - Article
C2 - 24184964
SN - 1672-0229
VL - 12
SP - 39
EP - 47
JO - Genomics, Proteomics & Bioinformatics
JF - Genomics, Proteomics & Bioinformatics
IS - 1
ER -