Mining High-Correlation Association Rules for Inferring Gene Regulation Networks

Authors:
Xuequn Shang;Qian Zhao;Zhanhuai Li
Affiliations:
Institute of Computer Science and Engineering, Northwestern Polytechnical University, Shaanxi, China 710072;Institute of Computer Science and Engineering, Northwestern Polytechnical University, Shaanxi, China 710072;Institute of Computer Science and Engineering, Northwestern Polytechnical University, Shaanxi, China 710072
Venue:
DaWaK '09 Proceedings of the 11th International Conference on Data Warehousing and Knowledge Discovery
Year:
2009

Citing 10
Cited 0

Mining association rules between sets of items in large databases

SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Finding Localized Associations in Market Basket Data

IEEE Transactions on Knowledge and Data Engineering
Biclustering of Expression Data

Proceedings of the Eighth International Conference on Intelligent Systems for Molecular Biology
Handling very large numbers of association rules in the analysis of microarray data

Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Carpenter: finding closed patterns in long biological datasets

Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
An efficient cluster and decomposition algorithm for mining association rules

Information Sciences—Informatics and Computer Science: An International Journal
Mining Frequent Closed Patterns in Microarray Data

ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
Predicting Protein-Protein Interactions by Association Mining

Information Systems Frontiers
Association analysis-based transformations for protein interaction networks: a function prediction case study

Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Large-scale regulatory network analysis from microarray data: modified Bayesian network learning and association rule mining

Decision Support Systems

Quantified Score

Hi-index	0.00

Visualization

Abstract

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.