Abstract
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.
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Seeja, K.R., Alam, M.A., Jain, S.K. (2008). Identification of Co-regulated Signature Genes in Pancreas Cancer- A Data Mining Approach. In: Huang, DS., Wunsch, D.C., Levine, D.S., Jo, KH. (eds) Advanced Intelligent Computing Theories and Applications. With Aspects of Theoretical and Methodological Issues. ICIC 2008. Lecture Notes in Computer Science, vol 5226. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87442-3_19
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DOI: https://doi.org/10.1007/978-3-540-87442-3_19
Publisher Name: Springer, Berlin, Heidelberg
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