An efficient algorithm for mining coherent patterns from heterogeneous microarrays

Xiang Zhang, Wei Wang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations

Abstract

DNA microarray techniques present a novel way for geneticists to monitor interactions among tens of thousands of genes simultaneously, and have become standard lab routines in gene discovery, disease diagnosis, and drug design. There has been extensive research on coherent subspace clustering of gene expressions measured under consistent experimental settings. This implies 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, which is shown effective for revealing truthful patterns and reducing spurious ones. We also develop an efficient and scalable hybrid approach that combines gene-pair based and sample-pair based pruning to generate F-clusters from multiple gene expression matrices simultaneously. The experimental results demonstrate that our model can discover significant clusters that may not be identified by previous models.

Original languageEnglish (US)
Title of host publication19th International Conference on Scientific and Statistical Database Management, SSDBM 2007
DOIs
StatePublished - 2007
Event19th International Conference on Scientific and Statistical Database Management, SSDBM 2007 - Banff, AB, Canada
Duration: Jul 9 2007Jul 11 2007

Publication series

NameProceedings of the International Conference on Scientific and Statistical Database Management, SSDBM
ISSN (Print)1099-3371

Other

Other19th International Conference on Scientific and Statistical Database Management, SSDBM 2007
CountryCanada
CityBanff, AB
Period7/9/077/11/07

All Science Journal Classification (ASJC) codes

  • Software
  • Applied Mathematics

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