Application of Simulated Annealing to the Biclustering of Gene Expression Data

• Published in: IEEE transactions on information technology in biomedicine Vol. 10; no. 3; pp. 519 - 525
• Main Authors: Bryan, K., Cunningham, P., Bolshakova, N.
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.07.2006
• Subjects: Algorithms; Animals; Artificial Intelligence; Biclustering; Biological system modeling; Cluster Analysis; Computer Simulation; Data analysis; Data mining; DNA; Evolutionary computation; Gene expression; Gene Expression - physiology; Gene Expression Profiling - methods; Humans; Information Storage and Retrieval - methods; Models, Biological; Multigene Family - physiology; Oligonucleotide Array Sequence Analysis - methods; Patient monitoring; Pattern Recognition, Automated - methods; Simulated annealing; Stochastic processes; Testing
• ISSN: 1089-7771; 1558-0032
• DOI: 10.1109/TITB.2006.872073

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