Separation of water artifacts in 2D NOESY protein spectra using congruent matrix pencils

Authors:
K. Stadlthanner;A. M. Tomé;F. J. Theis;E. W. Lang;W. Gronwald;H. R. Kalbitzer
Affiliations:
Institute of Biophysics, Neuro- and Bioinformatics Group, University of Regensburg, D-93040 Regensburg, Germany;Departamento de Electrónica e Telecomunicaçíes/IEETA, Universidade de Aveiro, P-3810 Aveiro, Portugal;Institute of Biophysics, Neuro- and Bioinformatics Group, University of Regensburg, D-93040 Regensburg, Germany;Institute of Biophysics, Neuro- and Bioinformatics Group, University of Regensburg, D-93040 Regensburg, Germany;Institute of Biophysics, University of Regensburg, D-93040 Regensburg, Germany;Institute of Biophysics, University of Regensburg, D-93040 Regensburg, Germany
Venue:
Neurocomputing
Year:
2006

Citing 14
Cited 3

Adaptive blind separation of independent sources: a deflation approach

Signal Processing
An information-maximization approach to blind separation and blind deconvolution

Neural Computation
Jacobi Angles for Simultaneous Diagonalization

SIAM Journal on Matrix Analysis and Applications
A fast fixed-point algorithm for independent component analysis

Neural Computation
The symmetric eigenvalue problem

The symmetric eigenvalue problem
Independent Component Analysis: Principles and Practice

Independent Component Analysis: Principles and Practice
Linear geometric ICA: fundamentals and algorithms

Neural Computation
Blind Source Separation Using a Matrix Pencil

IJCNN '00 Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 3 - Volume 3
Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications

Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications
Blind source separation via generalized eigenvalue decomposition

The Journal of Machine Learning Research
Blind Source Separation Using Temporal Predictability

Neural Computation
Separation of a mixture of chaotic signals

ICASSP '96 Proceedings of the Acoustics, Speech, and Signal Processing, 1996. on Conference Proceedings., 1996 IEEE International Conference - Volume 03
A blind source separation technique using second-order statistics

IEEE Transactions on Signal Processing
A matrix-pencil approach to blind separation of colorednonstationary signals

IEEE Transactions on Signal Processing

Denoising using local projective subspace methods

Neurocomputing
Hybridizing sparse component analysis with genetic algorithms for microarray analysis

Neurocomputing
Independent component analysis algorithms for microarray data analysis

Intelligent Data Analysis - Knowledge Discovery in Bioinformatics

Quantified Score

Hi-index	0.01

Visualization

Abstract

Multidimensional proton nuclear magnetic resonance spectra of biomolecules dissolved in aqueous solutions are usually contaminated by an intense water artifact. We discuss the application of a generalized eigenvalue decomposition (GEVD) method using a matrix pencil to solve the blind source separation (BSS) problem of removing the intense solvent peak and related artifacts. The method explores correlation matrices of the signals and their filtered versions in the frequency domain and implements a two-step algebraic procedure to solve the GEVD. Two-dimensional nuclear Overhauser enhancement spectroscopy (2D NOESY) of dissolved proteins is studied. Results are compared to those obtained with the SOBI [Belouchrani et al., IEEE Trans. Signal Process. 45(2) (1997) 434-444] algorithm which jointly diagonalizes several time-delayed correlation matrices and to those of the fastICA [Hyvarinen and Oja, Neural Comput. 9 (1996) 1483-1492] algorithm which exploits higher order statistical dependencies of random variables.