TY - GEN
T1 - Mining time-shifting co-regulation patterns from gene expression data
AU - Yin, G.
AU - Zhao, Yuhai
AU - Zhang, Bin
AU - Wang, Guoren
PY - 2007
Y1 - 2007
N2 - Previous work for finding patterns only focuses on grouping objects under the same subset of dimensions. Thus, an important bio-interesting pattern, i.e. time-shifting, will be ignored during the analysis of time series gene expression data. In this paper, we propose a new definition of coherent cluster for time series gene expression data called ts-cluster. The proposed model allows (1) the expression profiles of genes in a cluster to be coherent on different subsets of dimensions, i.e. these genes follow a certain time-shifting relationship, and (2) relative expression magnitude is taken into consideration instead of absolute one, which can tolerate the negative impact induced by "noise". This work is missed by previous research, which facilitates the study of regulatory relationships between genes. A novel algorithm is also presented and implemented to mine all the significant ts-clusters. Results experimented on both synthetic and real datasets show the ts-cluster algorithm is able to efficiently detect a significant amount of clusters missed by previous model, and these clusters are potentially of high biological significance.
AB - Previous work for finding patterns only focuses on grouping objects under the same subset of dimensions. Thus, an important bio-interesting pattern, i.e. time-shifting, will be ignored during the analysis of time series gene expression data. In this paper, we propose a new definition of coherent cluster for time series gene expression data called ts-cluster. The proposed model allows (1) the expression profiles of genes in a cluster to be coherent on different subsets of dimensions, i.e. these genes follow a certain time-shifting relationship, and (2) relative expression magnitude is taken into consideration instead of absolute one, which can tolerate the negative impact induced by "noise". This work is missed by previous research, which facilitates the study of regulatory relationships between genes. A novel algorithm is also presented and implemented to mine all the significant ts-clusters. Results experimented on both synthetic and real datasets show the ts-cluster algorithm is able to efficiently detect a significant amount of clusters missed by previous model, and these clusters are potentially of high biological significance.
UR - http://www.scopus.com/inward/record.url?scp=38049091170&partnerID=8YFLogxK
U2 - 10.1007/978-3-540-72524-4_10
DO - 10.1007/978-3-540-72524-4_10
M3 - Conference contribution
AN - SCOPUS:38049091170
SN - 9783540724834
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 62
EP - 73
BT - Advances in Data and Web Management - Joint 9th Asia-Pacific Web Conference, APWeb 2007 and 8th International Conference on Web-Age Information Management, WAIM 2007, Proceedings
PB - Springer Verlag
T2 - Joint 9th Asia-Pacific Web Conference on Advances in Data and Web Management, APWeb 2007 and 8th International Conference on Web-Age Information Management, WAIM 2007
Y2 - 16 June 2007 through 18 June 2007
ER -