Using optimisation techniques for discretizing rough set partitions
International Journal of Hybrid Intelligent Systems - Computational Models for Life Sciences
Tumor clustering using nonnegative matrix factorization with gene selection
IEEE Transactions on Information Technology in Biomedicine - Special section on biomedical informatics
Iterated local search for biclustering of microarray data
PRIB'10 Proceedings of the 5th IAPR international conference on Pattern recognition in bioinformatics
Measuring the quality of shifting and scaling patterns in biclusters
PRIB'10 Proceedings of the 5th IAPR international conference on Pattern recognition in bioinformatics
Parallelized Evolutionary Learning for Detection of Biclusters in Gene Expression Data
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
BiMine+: An efficient algorithm for discovering relevant biclusters of DNA microarray data
Knowledge-Based Systems
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In a gene expression data matrix, a bicluster is a submatrix of genes and conditions that exhibits a high correlation of expression activity across both rows and columns. The problem of locating the most significant bicluster has been shown to be NP-complete. Heuristic approaches such as Cheng and Church's greedy node deletion algorithm have been previously employed. It is to be expected that stochastic search techniques such as evolutionary algorithms or simulated annealing might improve upon such greedy techniques. In this paper we show that an approach based on simulated annealing is well suited to this problem, and we present a comparative evaluation of simulated annealing and node deletion on a variety of datasets. We show that simulated annealing discovers more significant biclusters in many cases. Furthermore, we also test the ability of our technique to locate biologically verifiable biclusters within an annotated set of genes