Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Set-Oriented Mining for Association Rules in Relational Databases
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Microarray Gene Expression Data Association Rules Mining Based On JG-Tree
DEXA '03 Proceedings of the 14th International Workshop on Database and Expert Systems Applications
ICDMW '06 Proceedings of the Sixth IEEE International Conference on Data Mining - Workshops
Parallel Mining of Association Rules from Gene Expression Databases
FSKD '07 Proceedings of the Fourth International Conference on Fuzzy Systems and Knowledge Discovery - Volume 03
High Confidence Rule Mining for Microarray Analysis
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Hypothesis-Driven Specialization of Gene Expression Association Rules
BIBM '07 Proceedings of the 2007 IEEE International Conference on Bioinformatics and Biomedicine
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Pancreas cancer is one of the most fatal among the cancers. The mortality rate is high due to the lack of tools for proper diagnosis and effective therapeutics. Identification of changes in gene expression in pancreas cancer may lead to the development of novel tools for diagnosis and effective treatment methodology. In this paper we present an association rule mining approach to identify the association between the genes that are either over expressed or under expressed in pancreas cancer compared to normal pancreas. We have used the SAGE data related to pancreas cancer. It is expected that the results will help in developing better treatment methodology for pancreas cancer and also for designing a low cost microarray chip for diagnosing pancreas cancer. The results have been validated in terms of Gene Ontology and the signature genes have been identified that match with published data.