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Utilization of gene ontology in semi-supervised clustering

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3 Author(s)
Duong D. Doan ; Faculty of Computer Science, University of New Brunswick, 540 Windsor Street, Fredericton, Canada ; Yunli Wang ; Youlian Pan

Semi-supervised clustering incorporating biological relevance as a prior knowledge has been favored over the past decade. However, selection of prior knowledge has been a challenge. We generate prior knowledge from Gene Ontology (GO) terms at different levels of GO hierarchy and use them to study their impact on the performance of subsequent clustering of microarray data by using MPCKMeans and GOFuzzy. We evaluate the performance by F-measure and the number of specific GO terms and transcription factors. The clustering result with prior knowledge generated from lower levels of GO hierarchy have higher F-measure and more number of specific GO terms and transcription factors. MPCKMeans with prior knowledge generated from multiple levels in the GO hierarchy outperforms GOFuzzy with prior knowledge from the first level in the GO hierarchy. A small amount (1-2%) of prior knowledge can improve semi-supervised clustering result substantially and the more specific prior knowledge is generally more efficient in guiding the semi-supervised clustering process.

Published in:

Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), 2011 IEEE Symposium on

Date of Conference:

11-15 April 2011