Smoothing Gene Expression Using Biological Networks

Gene expression (microarray) data have been used widely in bioinformatics. The expression data of a large number of genes from small numbers of subjects are used to identify informative biomarkers that may predict or help in diagnosing some disorders. More recently, increasing amounts of information from underlying relationships of the expressed genes have become available, and workers have started to investigate algorithms which can use such a priori information to improve classification or regression based on gene expression. In this paper, we describe three novel machine learning algorithms for regularizing (smoothing) microarray expression values defined on gene sets with known prior network or metric structures, and which exploit this gene interaction information. These regularized expression values can be used with any machine classifier with the goal of better classification. In this paper, standard smoothing (denoising) techniques previously developed for functions on Euclidean...

• Type: Journal
• Year: 2010
• Where: ICMLA
• Authors: Yue Fan, Mark A. Kon, Shinuk Kim, Charles DeLisi
• Keywords: Expression Values, Gene Expression, ICMLA 2010, Machine Learning, Smoothing