Please use this identifier to cite or link to this item: https://doi.org/10.1109/IRI.2011.6009538
Title: Mining transcriptional association rules from breast cancer profile data
Authors: Malpani, R.
Lu, M.
Zhang, D.
Sung, W.K. 
Keywords: Association rule mining
Data mining
Data preprocessing
Information Integration
Machine Learning
Issue Date: 2011
Source: Malpani, R.,Lu, M.,Zhang, D.,Sung, W.K. (2011). Mining transcriptional association rules from breast cancer profile data. Proceedings of the 2011 IEEE International Conference on Information Reuse and Integration, IRI 2011 : 154-159. ScholarBank@NUS Repository. https://doi.org/10.1109/IRI.2011.6009538
Abstract: 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. © 2011 IEEE.
Source Title: Proceedings of the 2011 IEEE International Conference on Information Reuse and Integration, IRI 2011
URI: http://scholarbank.nus.edu.sg/handle/10635/40514
ISBN: 9781457709661
DOI: 10.1109/IRI.2011.6009538
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