Automatic evolution of bi-clusters from microarray data using self-organized multi-objective evolutionary algorithm

In the current paper, a novel approach is proposed for bi-clustering of gene expression data using the fusion of differential evolution framework and self-organizing map (SOM), named as BiClustSMEA. Variable number of gene and condition cluster centers are encoded in different solutions of the population to determine the number of bi-clusters from a dataset in an automated way. The concept of SOM is utilized in designing new genetic operators for both gene and condition clusters to reach to the optimal solution in a faster way. In order to measure the goodness of a bi-clustering solution, three bi-cluster quality measures, mean squared error, row variance, and bi-cluster size, are optimized simultaneously using differential evolution as the underlying optimization strategy. The concept of polynomial mutation is incorporated in our framework to generate highly diverse solutions which in turn helps in faster convergence. The proposed approach is applied on two real-life microarray gene expression datasets and results are compared with various state-of-the-art techniques. Results obtained clearly illustrate that our approach extracts high-quality bi-clusters as compared to other methods and also it converges much faster than other competitors. Further, the obtained results are validated using statistical significance test and biological significance test.