Title :
Kernel-PCA denoising of artifact-free protein NMR spectra
Author :
Stadlthanner, K. ; Lang, E.W. ; Gruber, P. ; Theis, F.J. ; Tomé, A.M. ; Teixeira, A.R. ; Puntonet, C.G.
Author_Institution :
Inst. of Biophys., Univ. of Regensburg, Regensburg, Germany
Abstract :
Multidimensional 1H NMR spectra of biomolecules dissolved in light water are contaminated by an intense water artifact. Generalized eigenvalue decomposition methods using congruent matrix pencils are used to separate the water artefact from the protein spectra. Due to the statistical separation process, however, noise is introduced into the reconstructed spectra. Hence Kernel-based denoising techniques are discussed to obtain noise- and artifact-free 2D NOESY NMR spectra of proteins.
Keywords :
biological NMR; eigenvalues and eigenfunctions; macromolecules; matrix decomposition; molecular biophysics; principal component analysis; proteins; signal denoising; artifact free 2D NOESY NMR spectra; artifact free protein NMR spectra; biomolecules; congruent matrix pencils; generalized eigenvalue decomposition; intense water artifact; kernel PCA denoising; kernel based denoising; multidimensional NMR spectra; noise free 2D NOESY NMR spectra; statistical separation process; Eigenvalues and eigenfunctions; Kernel; Matrix decomposition; Noise reduction; Nuclear magnetic resonance; Principal component analysis; Proteins; Signal processing; Water pollution; Water resources;
Conference_Titel :
Neural Networks, 2004. Proceedings. 2004 IEEE International Joint Conference on
Print_ISBN :
0-7803-8359-1
DOI :
10.1109/IJCNN.2004.1380913
