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Mining transcriptional association rules from breast cancer profile data

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4 Author(s)
Malpani, R. ; Comput. Sci. Dept., California State Univ., Sacramento, CA, USA ; Lu, M. ; Du Zhang ; Wing Kin Sung

To gain insight into regulatory mechanisms underlying the transcription process of gene expressions, we need to understand the co-expressed gene sets under common regulatory mechanisms. Though computational methods have been developing to identify expression module, challenges still remain for cancer related gene expression profiling. In this paper, we have developed a method of data preprocessing and two different association rule mining approaches for discovering breast cancer regulatory mechanisms of gene module. Our data preprocessing task involved with two independent data sources: (a) a single breast cancer patient profile data file, (b) a candidate enhancer information data file. Using the integrated data, we also conducted four experiments of the association rule mining.

Published in:

Information Reuse and Integration (IRI), 2011 IEEE International Conference on

Date of Conference:

3-5 Aug. 2011

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