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A new physarum learner for network structure learning from biomedical data
Schön, T.; Stetter, M.; Tomé, A. M.; Lang, E. W.A novel structure learning algorithm for Bayesian Networks based on a Physarum Learner is presented. The length of the connections within an initially fully connected Physarum-Maze is taken as the inverse Pearson correlation coefficient between the connected nodes. The Physarum Learner then estimates the shortest indirect paths between each pair of nodes. In each iteration, a score of the surviving edges is inc...
SSA of biomedical signals: A linear invariant systems approach
Tomé, A.M.; Teixeira, Ana Rita; Figueiredo, Nuno; Santos, I.M.; Georgieva, P.; Lang, E.W.Singular spectrum analysis (SSA) is considered from a linear invariant systems perspective. In this terminology, the extracted components are considered as outputs of a linear invariant system which corresponds to finite impulse response (FIR) filters. The number of filters is determined by the embedding dimension.We propose to explicitly define the frequency response of each filter responsible for the selectio...
Brain connectivity analysis: a short survey
Lang, E. W.; Tomé, A. M.; Keck, I. R.; Górriz-Sáez, J. M.; Puntonet, C. G.This short survey the reviews recent literature on brain connectivity studies. It encompasses all forms of static and dynamic connectivity whether anatomical, functional, or effective. The last decade has seen an ever increasing number of studies devoted to deduce functional or effective connectivity, mostly from functional neuroimaging experiments. Resting state conditions have become a dominant experimental p...
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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