Open AccessAn approach to inferring transcriptional regulation among genes from large‐scale expression data
Author(s) -
Herrero Javier,
DíazUriarte Ramón,
Dopazo Joaquín
Publication year - 2003
Publication title -
comparative and functional genomics
Language(s) - English
Resource type - Journals
eISSN - 1532-6268
pISSN - 1531-6912
DOI - 10.1002/cfg.237
Subject(s) - inference , dna microarray , computer science , variety (cybernetics) , curse of dimensionality , data mining , gene regulatory network , expression (computer science) , scale (ratio) , machine learning , computational biology , artificial intelligence , gene , gene expression , biology , genetics , physics , quantum mechanics , programming language
The use of DNA microarrays opens up the possibility of measuring the expression levels of thousands of genes simultaneously under different conditions. Time‐course experiments allow researchers to study the dynamics of gene interactions. The inference of genetic networks from such measures can give important insights for the understanding of a variety of biological problems. Most of the existing methods for genetic network reconstruction require many experimental data points, or can only be applied to the reconstruction of small subnetworks. Here we present a method that reduces the dimensionality of the dataset and then extracts the significant dynamic correlations among genes. The method requires a number of points achievable in common time‐course experiments. Copyright © 2003 John Wiley & Sons, Ltd.