TY - GEN
T1 - A fuzzy c-means algorithm using a correlation metrics and gene ontology
AU - Zhang, Mingrui
AU - Therneau, Terry
AU - McKenzie, Michael A.
AU - Li, Peter
AU - Yang, Ping
PY - 2008
Y1 - 2008
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=77957940693&partnerID=8YFLogxK
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U2 - 10.1109/icpr.2008.4761672
DO - 10.1109/icpr.2008.4761672
M3 - Conference contribution
AN - SCOPUS:77957940693
SN - 9781424421756
T3 - Proceedings - International Conference on Pattern Recognition
BT - 2008 19th International Conference on Pattern Recognition, ICPR 2008
PB - Institute of Electrical and Electronics Engineers Inc.
ER -