Biclustering of Expression Data Using Simulated Annealing

In gene expression data a bicluster is a subset of genes and a subset of conditions which show correlating levels of expression. However, the problem of finding significant biclusters in gene expression data grows exponentially with the size of the dataset. This means that exhaustive search for good biclusters is not feasible in real datasets so greedy search techniques such as Cheng and Church’s node deletion algorithm have been used. It is to be expected that stochastic search techniques such as Genetic Algorithms or Simulated Annealing might produce better solutions than greedy search. In this paper we show that a Simulated Annealing approach is well suited to this problem and we present a comparative evaluation of Simulated Annealing and node deletion on a variety of datasets and show that Simulated Annealing discovers more significant biclusters in many cases.

• Type: Conference
• Year: 2005
• Where: CBMS
• Authors: Kenneth Bryan, Padraig Cunningham, Nadia Bolshakova
• Keywords: CBMS 2005, Gene Expression Data, Greedy Search, Medical Imaging, Simulated Annealing