Created by W.Langdon from gp-bibliography.bib Revision:1.7818
This paper proposes an evolutionary method of identifying the gene regulatory network represented as a differential equation system. As the technology in DNA micro arrays has developed, large quantities of gene's expression data are becoming more available. As a result, it is essential to get information as to the gene regulatory network from the observed data of gene's expression. Among many proposed models to describe a gene network, we have chosen the differential equation system since it can represent complex relations among components. In the previous studies \cite{Tominaga:2000:GECCO}, the form of the differential equation is being fixed during the learning so that the ultimate goal of the identification is to optimise parameters, i.e., coefficients, in the fixed equation. On the other hand, for the sake of the flexibility of the model, we allow an arbitrary form of functions in the right-hand side of the differential equation (eq. (1)).
dXi /dt = fi (X1 , X2 , . . . , Xn ) (1)
For this purpose, we use Genetic Programming (GP) and establish a GP-based identification of time series in terms of differential equation systems.",
Published as: A.K. Dunker, A. Konagaya, S. Miyano, and T.Takagi (eds.) {"}Genome Informatics 2000{"} Universal Academy Press, Tokyo, 2000",
Genetic Programming entries for Erina Sakamoto Hitoshi Iba