Optimization of cDNA microarray experimental designs using an evolutionary algorithm.
The cDNA microarray is an important tool for generating large datasets of gene expression measurements.An efficient design is critical to ensure that the experiment will be able to address relevant biological questions. Microarray experimental design can be treated as a multicriterion optimization problem. For this class of problems evolutionary algorithms (EAs) are well suited, as they can search the solution space and evolve a design that optimizes the parameters of interest based on their relative value to the researcher under a given set of constraints. This paper introduces the use of EAs for optimization of experimental designs of spotted microarrays using a weighted objective function. The EA and the various criteria relevant to design optimization are discussed. Evolved designs are compared with designs obtained through exhaustive search with results suggesting that the EA can find just as efficient optimal or near-optimal designs within atractable timeframe.
Gondro
Cedric
C
Institute for Genetics and Bioinformatics, University of New England, Armidale, NSW-2351, Australia.
Kinghorn
Brian P
BP
D000465:Algorithms; D018076:DNA, Complementary / Q000235:genetics*; D020869:Gene Expression Profiling / Q000379:methods*; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D017422:Sequence Analysis, DNA / Q000379:methods*; D012984:Software
D018076:DNA, Complementary