A survey of methods for classification of gene expression data using evolutionary algorithms.
The rapid increase in the quantity of available biologic data over the last decade, brought about by the introduction of massively parallel methods for gene expression measurements, has highlighted the need for more efficient computational techniques for analysis. This paper reviews the use of evolutionary algorithms (EAs) in connection with classification based on gene expression data matrices. Brief introductions to data classification methods and EAs are given, followed by a survey of studies dealing with the application of evolutionary algorithms to various (cancer related) data sets. The general conclusion, based on the published results surveyed here, is that EAs may constitute an efficient method for optimal gene selection, and can also help in reducing the size (number of features used) of classifiers. In many cases, the classification accuracy obtained using EAs, often in conjunction with other methods, represents a significant improvement over results obtained without the use of EAs. However, long-term, independent clinical follow-up studies will be essential to validate prognostic markers identified by the use of EA-based methods.
Wahde
Mattias
M
Department of Applied Mechanics, Chalmers University of Technology, SE 412 96, Göteborg, Sweden. mattias.wahde@chalmers.se
Szallasi
Zoltan
Z
D000465:Algorithms; D005075:Biological Evolution; D016000:Cluster Analysis; D020869:Gene Expression Profiling / Q000379:methods*; D006801:Humans; D009369:Neoplasms / Q000235:genetics; D016571:Neural Networks, Computer