An evolutionary gene expression microarray clustering algorithm based on optimized experimental conditions

Entities of the real world require partition into groups based on even feature of each entity. Clusters are analyzed to make the groups homologous and well separated. Many algorithms have been developed to tackle clustering problems and are very much needed in our application area of gene expression profile analysis in bioinformatics. It is often difficult to group the data in the real world clearly since there is no clear boundary of clustering. Gene clustering possesses the same problem as they contain multiple functions and can belong to multiple clusters. Hence one sample is assigned to multiple clusters. A variety of clustering techniques have been applied to microarray data in bio-informatics research. We have proposed in this paper an easy to implement evolutionary clustering algorithm based on optimized number of experimental conditions for each individual cluster for which the elements of that group produced similar expression and then compared its performance with some of the previously proposed clustering algorithm for some real life data that proves its superiority compared to the others. The proposed algorithm will produce some overlapping clusters which reimposes the fact that a gene can participate in multiple biological processes.