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Advances in Bioinformatics
Volume 2009, Article ID 284251, 10 pages
http://dx.doi.org/10.1155/2009/284251
Research Article

The KM-Algorithm Identifies Regulated Genes in Time Series Expression Data

1Department of Mathematics, San Jose State University, One Washington Square, San Jose, CA 95192, USA
2Department of Statistics, Purdue University, 150 N. University Street, West Lafayette, IN 47907, USA

Received 14 February 2009; Revised 12 June 2009; Accepted 9 July 2009

Academic Editor: Zhongming Zhao

Copyright © 2009 Martina Bremer and R. W. Doerge. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

We present a statistical method to rank observed genes in gene expression time series experiments according to their degree of regulation in a biological process. The ranking may be used to focus on specific genes or to select meaningful subsets of genes from which gene regulatory networks can be built. Our approach is based on a state space model that incorporates hidden regulators of gene expression. Kalman (K) smoothing and maximum (M) likelihood estimation techniques are used to derive optimal estimates of the model parameters upon which a proposed regulation criterion is based. The statistical power of the proposed algorithm is investigated, and a real data set is analyzed for the purpose of identifying regulated genes in time dependent gene expression data. This statistical approach supports the concept that meaningful biological conclusions can be drawn from gene expression time series experiments by focusing on strong regulation rather than large expression values.