title: Increasing the efficiency of fuzzy logic-based gene expression data analysis.
abstract: DNA microarray technology can accommodate a multifaceted analysis of the expression of genes in an organism. The wealth of spatiotemporal data generated by this technology allows researchers to potentially reverse engineer a particular genetic network. "Fuzzy logic" has been proposed as a method to analyze the relationships between genes and help decipher a genetic network. This method can identify interacting genes that fit a known "fuzzy" model of gene interaction by testing all combinations of gene expression profiles. This paper introduces improvements made over previous fuzzy gene regulatory models in terms of computation time and robustness to noise. Improvement in computation time is achieved by using a cluster analysis as a preprocessing method to reduce the total number of gene combinations analyzed. This approach speeds up the algorithm by a factor of 50% with minimal effect on the results. The model's sensitivity to noise is reduced by implementing appropriate methods of "fuzzy rule aggregation" and "conjunction" that produce reliable results in the face of minor changes in model input.
authors.affiliation: Intelligent Systems Laboratory, Department of Electrical and Computer Engineering, University of Maine, Orono, Maine 04469, USA. ressom@eece.maine.edu
authors.forename: Habtom
authors.initials: H
authors.lastname: Ressom
authors.forename: Robert
authors.initials: R
authors.lastname: Reynolds
authors.forename: Rency S
authors.initials: RS
authors.lastname: Varghese
mesh_terms: D000818:Animals; D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods* / Q000592:standards / Q000706:statistics & numerical data / Q000639:trends*; D017143:Fuzzy Logic; D020869:Gene Expression Profiling / Q000379:methods* / Q000592:standards / Q000706:statistics & numerical data / Q000639:trends*; D006801:Humans