Construction of genetic network using evolutionary algorithm and combined fitness function.
This paper proposes a method to capture the dynamics in gene expression data using S-system formalism and construct genetic network models. Our purposed method exploits the probabilistic heuristic search and divide-and-conquer approach to estimate the network structure. In evaluating the network structure, we attempt a primitive integration of other knowledge to the statistical criterion. The Z-score is used to analyze the robust and significant parameters from stochastic search results. We evaluated the proposed method on artificially generated data and E.coli mRNA expression data.
Shin
Ando
A
Dept. of Electronics, School of Engineering, University of Tokyo, Japan. ando@iba.k.u-tokyo.ac.jp
Iba
Hitoshi
H
D000465:Algorithms; D019143:Evolution, Molecular; D008957:Models, Genetic; D009415:Nerve Net; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods; D015203:Reproducibility of Results