A fuzzy c-means algorithm using a correlation metrics and gene ontology

Mingrui Zhang, Terry Therneau, Michael A. McKenzie, Peter Li, Ping Yang

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

5 Scopus citations

Abstract

A fuzzy c-means algorithm was adapted for analyzing microarray data. The adaptation consisted of initialization of fuzzy centroids using gene ontology information and the use of Pearson correlation distance in the objective function. To initialize fuzzy centroids, we classified genes based on gene ontology terms and used the classified genes as initial fuzzy clusters. Pearson correlation distance becomes 0 if two genes are either positively or negatively correlated. The algorithm was applied to Yeast and lung cancer microarray datasets. It outperformed the conventional fuzzy c-means algorithm by associating more genes to functional groups.

Original languageEnglish (US)
Title of host publication2008 19th International Conference on Pattern Recognition, ICPR 2008
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Print)9781424421756
DOIs
StatePublished - Jan 1 2008

Publication series

NameProceedings - International Conference on Pattern Recognition
ISSN (Print)1051-4651

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition

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    Zhang, M., Therneau, T., McKenzie, M. A., Li, P., & Yang, P. (2008). A fuzzy c-means algorithm using a correlation metrics and gene ontology. In 2008 19th International Conference on Pattern Recognition, ICPR 2008 [4761672] (Proceedings - International Conference on Pattern Recognition). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/icpr.2008.4761672