期刊
JOURNAL OF THEORETICAL BIOLOGY
卷 261, 期 2, 页码 290-293出版社
ACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD
DOI: 10.1016/j.jtbi.2009.07.017
关键词
Protein functional class prediction; Global encoding; Nearest neighbor algorithm; Physiochemical property
资金
- National Nature Science Foundation of China [10571019, 60873184]
- National Nature Science Foundation of Hunan province [07JJ5080, 06JJ2090]
A key goal of the post-genomic era is to determine protein functions. In this paper, we proposed a global encoding method of protein sequence (GE) to descript global information of amino acid sequence, and then assign protein functional class using machine learning methods nearest neighbor algorithm (NNA). We predicted the function of 1818 Saccharomyces cerevisiae proteins which was used in Vazquez's global optimization method (GOM) except eight proteins which cannot get from the data base now or whose sequence length is too short. Using our approach, the computed accuracy is better than Vazquez's global optimization method (GOM) in some cases. The experiment results show that our new method is efficient to predict functional class of unknown proteins. (C) 2009 Elsevier Ltd. All rights reserved.
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