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Knowledge-based gene expression classification via matrix factorization
Schachtner, R.; Lutter, D.; Knollmüller, P.; Tomé, A. M.; Theis, F. J.; Schmitz, G.; Stetter, M.; Gómez Vilda, P.; Lang, E. W.Motivation: Modern machine learning methods based on matrix decomposition techniques, like independent component analysis (ICA) or non-negative matrix factorization (NMF), provide new and efficient analysis tools which are currently explored to analyze gene expression profiles. These exploratory feature extraction techniques yield expression modes (ICA) or metagenes (NMF). These extracted features are considere...
Hybridizing sparse component analysis with genetic algorithms for microarray an...
Stadlthanner, K.; Theis, F. J.; Lang, E. W.; Tomé, A. M.; Puntonet, C. G.; Górriz, J. M.Nonnegative Matrix Factorization (NMF) has proven to be a useful tool for the analysis of nonnegative multivariate data. However, it is known not to lead to unique results when applied to Blind Source Separation (BSS) problems. In this paper we present an extension of NMF capable of solving the BSS problem when the underlying sources are sufficiently sparse. In contrast to most well-established BSS methods, the...
A matrix pencil to the blind source separation of artifacts in 2D NMR spectra
Stadlthanner, K.; Theis, F. J.; Lang, E. W.; Tomé, A. M.; Gronwald, W.; Kalbitzer, H.-R.Multidimensional proton nmr spectra of biomolecules dissolved in aqueous solutions are usually contaminated by an intense water artifact. We discuss the application of the generalized eigenvalue decomposition (GEVD) method using a matrix pencil to solve the blind source separation problem of removing the intense solvent peak and related artifacts. 2D NOESY spectra of simple solutes as well as dissolved proteins...
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