Journal
PLOS ONE
Volume 9, Issue 3, Pages -Publisher
PUBLIC LIBRARY SCIENCE
DOI: 10.1371/journal.pone.0090962
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Funding
- Program for New Century Excellent Talents in University [NCET-10-0365]
- National Nature Science Foundation of China [60973082, 11171369, 61272395, 61370171]
- National Nature Science Foundation of Hunan Province [12JJ2041]
- Planned Science and Technology Project of Hunan Province [2009FJ3195, 2012FJ2012]
- Fundamental Research Funds for the Central Universities, Hunan university
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Time-course gene expression datasets, which record continuous biological processes of genes, have recently been used to predict gene function. However, only few positive genes can be obtained from annotation databases, such as gene ontology (GO). To obtain more useful information and effectively predict gene function, gene annotations are clustered together to form a learnable and effective learning system. In this paper, we propose a novel multi-instance hierarchical clustering (MIHC) method to establish a learning system by clustering GO and compare this method with other learning system establishment methods. Multi-label support vector machine classifier and multi-label K-nearest neighbor classifier are used to verify these methods in four yeast time-course gene expression datasets. The MIHC method shows good performance, which serves as a guide to annotators or refines the annotation in detail.
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