title: Computing the maximum similarity bi-clusters of gene expression data.
abstract: BACKGROUND
  Bi-clustering is an important approach in microarray data analysis. The underlying bases for using bi-clustering in the analysis of gene expression data are (1) similar genes may exhibit similar behaviors only under a subset of conditions, not all conditions, (2) genes may participate in more than one function, resulting in one regulation pattern in one context and a different pattern in another. Using bi-clustering algorithms, one can obtain sets of genes that are co-regulated under subsets of conditions.
  
  
  RESULTS
  We develop a polynomial time algorithm to find an optimal bi-cluster with the maximum similarity score. To our knowledge, this is the first formulation for bi-cluster problems that admits a polynomial time algorithm for optimal solutions. The algorithm works for a special case, where the bi-clusters are approximately squares. We then extend the algorithm to handle various kinds of other cases. Experiments on simulation data and real data show that the new algorithms outperform most of the existing methods in many cases. Our new algorithms have the following advantages: (1) no discretization procedure is required, (2) performs well for overlapping bi-clusters and (3) works well for additive bi-clusters.
  
  
  BACKGROUND
  The software is available at http://www.cs.cityu.edu.hk/~liuxw/msbe/help.html.
authors.affiliation: Department of Computer Science, City University of Hong Kong, Kowloon, Hong Kong.
authors.forename: Xiaowen
authors.initials: X
authors.lastname: Liu
authors.forename: Lusheng
authors.initials: L
authors.lastname: Wang
mesh_terms: D000465:Algorithms; D002453:Cell Cycle / Q000235:genetics; D016000:Cluster Analysis; D019295:Computational Biology; D003627:Data Interpretation, Statistical; D030561:Databases, Nucleic Acid / Q000706:statistics & numerical data; D005260:Female; D020869:Gene Expression Profiling / Q000706:statistics & numerical data*; D006801:Humans; D008957:Models, Genetic; D015233:Models, Statistical