Mining coherent patterns from heterogeneous microarray data

Authors:
Xiang Zhang;Wei Wang
Affiliations:
University of North Carolina at Chapel Hill, Chapel Hill, NC;University of North Carolina at Chapel Hill, Chapel Hill, NC
Venue:
CIKM '06 Proceedings of the 15th ACM international conference on Information and knowledge management
Year:
2006

Citing 3
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Abstract

Microarray technology is a powerful tool for geneticists to monitor interactions among tens of thousands of genes simultaneously. There has been extensive research on coherent subspace clustering of gene expressions measured under consistent experimental settings. However, these methods assume that all experiments are run using the same batch of microarray chips with similar characteristics of noise. Algorithms developed under this assumption may not be applicable for analyzing data collected from heterogeneous settings, where the set of genes being monitored may be different and expression levels may be not directly comparable even for the same gene. In this paper, we propose a model, F-cluster, for mining subspace coherent patterns from heterogeneous gene expression data. We compare our model with previously proposed models. We analyze the search space of the problem and give a naïve solution for it.