title: Efficient mining differential co-expression biclusters in microarray datasets.
abstract: BACKGROUND
  Biclustering algorithm can find a number of co-expressed genes under a set of experimental conditions. Recently, differential co-expression bicluster mining has been used to infer the reasonable patterns in two microarray datasets, such as, normal and cancer cells.
  
  
  METHODS
  In this paper, we propose an algorithm, DECluster, to mine Differential co-Expression biCluster in two discretized microarray datasets. Firstly, DECluster produces the differential co-expressed genes from each pair of samples in two microarray datasets, and constructs a differential weighted undirected sample-sample relational graph. Secondly, the differential biclusters are generated in the above differential weighted undirected sample-sample relational graph. In order to mine maximal differential co-expression biclusters efficiently, we design several pruning techniques for generating maximal biclusters without candidate maintenance.
  
  
  RESULTS
  The experimental results show that our algorithm is more efficient than existing methods. The performance of DECluster is evaluated by empirical p-value and gene ontology, the results show that our algorithm can find more statistically significant and biological differential co-expression biclusters than other algorithms.
  
  
  CONCLUSIONS
  Our proposed algorithm can find more statistically significant and biological biclusters in two microarray datasets than the other two algorithms.
authors.affiliation: School of Computer Science and Technology, Northwestern Polytechnical University, Xi'an 710072, China. riyushui@gmail.com
authors.forename: Miao
authors.initials: M
authors.lastname: Wang
authors.forename: Xuequn
authors.initials: X
authors.lastname: Shang
authors.forename: Xiaoyuan
authors.initials: X
authors.lastname: Li
authors.forename: Wenbin
authors.initials: W
authors.lastname: Liu
authors.forename: Zhanhuai
authors.initials: Z
authors.lastname: Li
mesh_terms: D000236:Adenoma / Q000235:genetics; D000375:Aging / Q000235:genetics; D000465:Algorithms; D000818:Animals; D002277:Carcinoma / Q000235:genetics; D016000:Cluster Analysis; D057225:Data Mining / Q000379:methods; D030541:Databases, Genetic; D015870:Gene Expression; D006801:Humans; D051379:Mice; D008810:Mice, Inbred C57BL; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D011897:Random Allocation; D012984:Software