title: Efficiently mining time-delayed gene expression patterns.
abstract: Unlike pattern-based biclustering methods that focus on grouping objects in the same subset of dimensions, in this paper, we propose a novel model of coherent clustering for time-series gene expression data, i.e., time-delayed cluster (td-cluster). Under this model, objects can be coherent in different subsets of dimensions if these objects follow a certain time-delayed relationship. Such a cluster can discover the cycle time of gene expression, which is essential in revealing gene regulatory networks. This paper is the first attempt to mine time-delayed gene expression patterns from microarray data. A novel algorithm is also presented and implemented to mine all significant td-clusters. Our experimental results show following two results: 1) the td-cluster algorithm can detect a significant amount of clusters that were missed by previous models, and these clusters are potentially of high biological significance and 2) the td-cluster model and algorithm can easily be extended to 3-D gene x sample x time data sets to identify 3-D td-clusters.
authors.affiliation: School of Information Science and Engineering, and Key Laboratory ofMedical Image Computing, Northeastern University, Shenyang 110004, China. wanggr@mail.neu.edu.cn
authors.forename: Guoren
authors.initials: G
authors.lastname: Wang
authors.forename: Linjun
authors.initials: L
authors.lastname: Yin
authors.forename: Yuhai
authors.initials: Y
authors.lastname: Zhao
authors.forename: Keming
authors.initials: K
authors.lastname: Mao
mesh_terms: D000465:Algorithms; D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods; D057225:Data Mining / Q000379:methods*; D020869:Gene Expression Profiling / Q000379:methods*; D020411:Oligonucleotide Array Sequence Analysis; D010363:Pattern Recognition, Automated / Q000379:methods*