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Computers and Chemical Engineering 29 (2005) 535546 Selecting maximally informative genes

Summary: Computers and Chemical Engineering 29 (2005) 535546
Selecting maximally informative genes
Ioannis P. Androulakis
Biomedical Engineering Department, Rutgers University, 617 Browser Road,
Piscataway, NJ 08854, USA
Received 22 September 2003; received in revised form 2 March 2004
Microarray experiments are emerging as one of the main driving forces in modern biology. By allowing the simultaneous monitoring of
the expression of the entire genome for a given organism, array experiments provide tremendous insight into the fundamental biological
processes that translate genetic information. One of the major challenges is to identify computationally efficient and biologically meaningful
analysis approaches to extract the most informative and unbiased components of the microarray data. This process is complicated by the fact
that a number of uncertainties are associated with array experiments. Therefore, the assumption of the existence of a unique computational
descriptive model needs to be challenged. In this paper, we introduce a framework that integrates machine learning and optimization techniques
for the selection of maximally informative genes in microarray expression experiments. The fundamental premise of the approach is that
maximally informative genes are the ones that lead to least complex descriptive and predictive models. We propose a methodology, based on
decision trees, which identifies ensembles of groups of maximally informative genes. We raise a number of computational issues that need to
be comprehensively addressed and illustrate the approach by analyzing recently published microarray experimental data.
2004 Elsevier Ltd. All rights reserved.
Keywords: Maximally informative genes; Microarray experiments; Genetic information; Machine learning; Optimization
1. Microarray experiments: brief introduction and


Source: Androulakis, Ioannis (Yannis) - Biomedical Engineering Department & Department of Chemical and Biochemical Engineering, Rutgers University


Collections: Engineering; Biology and Medicine