Title :
Mining Positive and Negative Co-regulation Patterns from Microarray Data
Author :
Zhao, Yuhai ; Wang, Guoren ; Yin, Ying ; Yu, Ge
Author_Institution :
Inst. of Comput. Syst., Northeastern Univ., Shenyang
Abstract :
Currently, pattern-based and tendency-based models are very popular for clustering co-regulated genes. In this paper, we propose another novel model, namely g-Cluster. The proposed model has the following advantages: (1) find positive and negative co-regulated genes in a shot, (2) get away from the restriction of magnitude transformation relationship among genes, and (3) guarantee quality of clusters and significance of regulations using a novel similarity measurement gCode and two user-specified thresholds, called wave constraint threshold and regulation threshold respectively. We also design a novel tree-based clustering algorithm, FBTD, combined with efficient pruning rules to identify all maximal g-Clusters. The extensive experiments on real and synthetic datasets show that (1) our algorithm can effectively and efficiently find an amount of co-regulated gene clusters missed by previous models, which are potentially of high biological significance, and (2) our algorithm is superior to the existing approaches
Keywords :
biology computing; data mining; genetics; pattern clustering; trees (mathematics); FBTD; co-regulated gene clustering; magnitude transformation relationship; maximal g-clusters; microarray data mining; negative co-regulation patterns; positive co-regulation patterns; regulation threshold; synthetic dataset; tree-based clustering algorithm; user-specified threshold; wave constraint threshold; Algorithm design and analysis; Biological system modeling; Clustering algorithms; Clustering methods; Coherence; Data analysis; Gene expression; Pareto analysis; Pattern analysis; Subspace constraints;
Conference_Titel :
BioInformatics and BioEngineering, 2006. BIBE 2006. Sixth IEEE Symposium on
Conference_Location :
Arlington, VA
Print_ISBN :
0-7695-2727-2
DOI :
10.1109/BIBE.2006.253320