Mining coherent patterns from heterogeneous microarray data

Xiang Zhang, Wei Wang

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

4 Scopus citations

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 nave solution for it.

Original languageEnglish (US)
Title of host publicationProceedings of the 15th ACM Conference on Information and Knowledge Management, CIKM 2006
Pages838-839
Number of pages2
DOIs
StatePublished - Dec 1 2006
Event15th ACM Conference on Information and Knowledge Management, CIKM 2006 - Arlington, VA, United States
Duration: Nov 6 2006Nov 11 2006

Other

Other15th ACM Conference on Information and Knowledge Management, CIKM 2006
CountryUnited States
CityArlington, VA
Period11/6/0611/11/06

All Science Journal Classification (ASJC) codes

  • Decision Sciences(all)
  • Business, Management and Accounting(all)

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