title: A new clustering method for microarray data analysis.
abstract: A novel clustering approach is introduced to overcome data missing and inconsistency of gene expression levels under different conditions in the stage of clustering. It is based on the so-called smooth score, which is defined for measuring the deviation of the expression level of a gene and the average expression level of all the genes involved under a condition. We present an efficient greedy algorithm for finding clusters with smooth score below a threshold after studying its computational complexity. The algorithm was tested intensively on random matrixes and a yeast data. It was shown to perform well in finding co-regulation patterns in a test with the yeast data.
authors.affiliation: Department of Mathematics and LIT, National University of Singapore, Singapore. matzlx@nus.edu.sg
authors.forename: Louxin
authors.initials: L
authors.lastname: Zhang
authors.forename: Song
authors.initials: S
authors.lastname: Zhu
mesh_terms: D000465:Algorithms; D016000:Cluster Analysis; D020869:Gene Expression Profiling / Q000379:methods*; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D029701:Saccharomyces cerevisiae Proteins / Q000235:genetics / Q000378:metabolism; D016415:Sequence Alignment / Q000379:methods*; D017422:Sequence Analysis, DNA / Q000379:methods*
chemical_list: D029701:Saccharomyces cerevisiae Proteins