title: Evolving fuzzy rules to model gene expression.
abstract: This paper develops an algorithm that extracts explanatory rules from microarray data, which we treat as time series, using genetic programming (GP) and fuzzy logic. Reverse polish notation is used (RPN) to describe the rules and to facilitate the GP approach. The algorithm also allows for the insertion of prior knowledge, making it possible to find sets of rules that include the relationships between genes already known. The algorithm proposed is applied to problems arising in the construction of gene regulatory networks, using two different sets of real data from biological experiments on the Arabidopsis thaliana cold response and the rat central nervous system, respectively. The results show that the proposed technique can fit data to a pre-defined precision even in situations where the data set has thousands of features but only a limited number of points in time are available, a situation in which traditional statistical alternatives encounter difficulties, due to the scarcity of time points.
authors.affiliation: FSMA-RJ, R Monte Elíseo S/N, CEP 27943-180, Macaé, RJ, Brazil. rlinden@pobox.com
authors.forename: Ricardo
authors.initials: R
authors.lastname: Linden
authors.forename: Amit
authors.initials: A
authors.lastname: Bhaya
mesh_terms: D000465:Algorithms; D000818:Animals; D017360:Arabidopsis / Q000235:genetics; D016000:Cluster Analysis; D017143:Fuzzy Logic; D020869:Gene Expression Profiling / Q000706:statistics & numerical data; D046228:Microarray Analysis / Q000706:statistics & numerical data*; D008957:Models, Genetic; D009154:Mutation; D051381:Rats; D049490:Systems Biology