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CLUGO: a clustering algorithm for automated functional annotations based on gene ontology

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3 Author(s)
In-Yee Lee ; Dept. of Electr. Eng., National Taiwan Univ., Taiwan ; Jan-Ming Ho ; Ming-Syan Chen

We address the issue of providing highly informative and comprehensive annotations using information revealed by the structured vocabularies of gene ontology (GO). For a target, a set of candidate terms for inferring target properties is collected and form a unique distribution on the GO directed acyclic graph (DAG). We propose a novel ontology-based clustering algorithm $CLUGO, which considers GO hierarchical characteristics and the clustering of term distributions. By identifying significant groups in the distributions, CLUGO assigns comprehensive and correct annotations for a target. According to the results of experiments with automated sequence functional annotations, CLUGO represents a considerable improvement over our previous work - GOMIT in terms of recall while maintaining a similar level of precision. We conclude that given a GO candidate term distribution, CLUGO is an efficient ontology-based clustering algorithm for selecting comprehensive and correct annotations.

Published in:

Data Mining, Fifth IEEE International Conference on

Date of Conference:

27-30 Nov. 2005