Identification of nuclear magnetic resonance signals via gaussian mixture decomposition

Authors:
Martin Krone;Frank Klawonn;Thorsten Lührs;Christiane Ritter
Affiliations:
Department of Computer Science, Ostfalia University of Applied Sciences, Wolfenbuettel and Division of Structural Biology, Helmholtz-Centre for Infection Research, Braunschweig, Germany;Department of Computer Science, Ostfalia University of Applied Sciences, Wolfenbuettel and Division of Structural Biology, Helmholtz-Centre for Infection Research, Braunschweig, Germany;Division of Structural Biology, Helmholtz-Centre for Infection Research, Braunschweig, Germany;Division of Structural Biology, Helmholtz-Centre for Infection Research, Braunschweig, Germany
Venue:
IDA'11 Proceedings of the 10th international conference on Advances in intelligent data analysis X
Year:
2011

Citing 4
Cited 0

Neural networks for the peak-picking of nuclear magnetic resonance spectra

Neural Networks
Estimation for the number of components in a mixture model using stepwise split-and-merge EM algorithm

Pattern Recognition Letters
Genetic-Based EM Algorithm for Learning Gaussian Mixture Models

IEEE Transactions on Pattern Analysis and Machine Intelligence
PICKY

Bioinformatics

Quantified Score

Hi-index	0.00

Visualization

Abstract

Nuclear Magnetic Resonance spectroscopy is a powerful technique for the determination of protein structures and has been supported by computers for decades. One important step during this process is the identification of resonances in the data. However, due to noise, overlapping effects and artifacts occuring during the measurements, many algorithms fail to identify resonances correctly. In this paper, we present a novel interpretation of the data as a sample drawn from a mixture of bivariate Gaussian distributions. Therefore, the identification of resonances can be reduced to a Gaussian mixture decomposition problem which is solved with the help of the Expectation-Maximization algorithm. A program in the Java programming language that exploits an implementation of this algorithm is described and tested on experimental data. Our results indicate that this approach offers valuable information such as an objective measure on the likelihood of the identified resonances.