Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Genetic Algorithms and Grouping Problems
Genetic Algorithms and Grouping Problems
Stochastic Complexity in Statistical Inquiry Theory
Stochastic Complexity in Statistical Inquiry Theory
Machine Learning
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Selecting Models from Data: AI and Statistics IV
Selecting Models from Data: AI and Statistics IV
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A critical challenge of the postgenomic era is to understand how genes are differentially regulated in and between genetic networks. The fact that such co-regulated genes may be differentially regulated suggests that subtle differences in the shared cis-acting regulatory elements are likely significant, however it is unknown which of these features increase or reduce expression of genes. In principle, this expression can be measured by microarray experiments, though they incorporate systematic errors, and moreover produce a limited classification (e.g. up/down regulated genes). In this work, we present an unsupervised machine learning method to tackle the complexities governing gene expression, which considers gene expression data as one feature among many. It analyzes features concurrently, recognizes dynamic relations and generates profiles, which are groups of promoters sharing common features. The method makes use of multiobjective techniques to evaluate the performance of profiles, and has a multimodal approach to produce alternative descriptions of same expression target. We apply this method to probe the regulatory networks governed by the PhoP/PhoQ two-component system in the enteric bacteria Escherichia coli and Salmonella enterica. Our analysis uncovered profiles that were experimentally validated, suggesting correlations between promoter regulatory features and gene expression kinetics measured by green fluorescent protein (GFP) assays.