TY - GEN
T1 - Mining high-correlation association rules for inferring gene regulation networks
AU - Shang, Xuequn
AU - Zhao, Qian
AU - Li, Zhanhuai
PY - 2009
Y1 - 2009
N2 - Construction gene regulation networks can provide insights into the understanding the molecular mechanisms underlying important biological processes. We present a novel association rule mining for building large-scale gene regulation networks from microarray data. Gene expression microarray data typically contains a very high gene dimension and a very low sample size, rendering a great challenge for existing association rule mining algorithms. In this paper, we develop a novel algorithm, HCMiner, to mine high-correlation association rules from microarray data. HCMiner initially overlapping partitions the dimension of genes according to their correlations and introduces the support-free framework for mining association rules. Several experiments on Yeast dataset show that the proposed algorithm outperforms existing algorithms with respect to scalability and effectiveness.
AB - Construction gene regulation networks can provide insights into the understanding the molecular mechanisms underlying important biological processes. We present a novel association rule mining for building large-scale gene regulation networks from microarray data. Gene expression microarray data typically contains a very high gene dimension and a very low sample size, rendering a great challenge for existing association rule mining algorithms. In this paper, we develop a novel algorithm, HCMiner, to mine high-correlation association rules from microarray data. HCMiner initially overlapping partitions the dimension of genes according to their correlations and introduces the support-free framework for mining association rules. Several experiments on Yeast dataset show that the proposed algorithm outperforms existing algorithms with respect to scalability and effectiveness.
UR - https://www.scopus.com/pages/publications/70349307041
U2 - 10.1007/978-3-642-03730-6_20
DO - 10.1007/978-3-642-03730-6_20
M3 - 会议稿件
AN - SCOPUS:70349307041
SN - 3642037291
SN - 9783642037290
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 244
EP - 255
BT - Data Warehousing and Knowledge Discovery - 11th International Conference, DaWaK 2009, Proceedings
T2 - 11th International Conference on Data Warehousing and Knowledge Discovery, DaWaK 2009
Y2 - 31 August 2009 through 2 September 2009
ER -