Regularized estimation of large-scale gene association networks using graphical gaussian models

Krämer, Nicole; Schäfer, Juliane und Boulesteix, Anne-Laure (2009): Regularized estimation of large-scale gene association networks using graphical gaussian models. In: BMC Bioinformatics, Bd. 10: S. 1-24

Abstract

Graphical Gaussian models are popular tools for the estimation of (undirected) gene association networks from microarray data. A key issue when the number of variables greatly exceeds the number of samples is the estimation of the matrix of partial correlations. Since the (Moore-Penrose) inverse of the sample covariance matrix leads to poor estimates in this scenario, standard methods are inappropriate and adequate regularization techniques are needed. Popular approaches include biased estimates of the covariance matrix and high-dimensional regression schemes, such as the Lasso and Partial Least Squares.

• Dokumententyp: Zeitschriftenartikel
• Fakultät: Mathematik, Informatik und Statistik > Statistik ; Mathematik, Informatik und Statistik > Statistik > Lehrstühle/Arbeitsgruppen > Assoziierte Personen
• Themengebiete: 500 Naturwissenschaften und Mathematik > 510 Mathematik
• Sprache: Englisch