Journal
BIOCHEMICAL AND BIOPHYSICAL RESEARCH COMMUNICATIONS
Volume 380, Issue 2, Pages 318-322Publisher
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.bbrc.2009.01.077
Keywords
Bioinformatics; Proteomics; Protein-protein interactions; KNNs; Feature selection
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Funding
- National Natural Science Foundation of China [20503015, 30672671]
- Shanghai Leading Academic Discipline Project [J50101]
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Based on pseudo amino acid (PseAA) composition and a novel hybrid feature selection frame, this paper presents a computational system to predict the PPIs (protein-protein interactions) using 8796 protein pairs. These pairs are coded by PseAA composition, resulting in 114 features. A hybrid feature selection system, mRMR-KNNs-wrapper, is applied to obtain an optimized feature set by excluding poor-performed and/or redundant features, resulting in 103 remaining features. Using the optimized 103-feature subset, a prediction model is trained and tested in the k-nearest neighbors (KNNs) learning system. This prediction model achieves an overall accurate prediction rate of 76.18%, evaluated by 10-fold cross-validation test, which is 1.46% higher than using the initial 114 features and is 6.51% higher than the 20 features, coded by amino acid compositions. The PPIs predictor, developed for this research, is available for public use at http://chemdata.shu.edu.cn/ppi. (c) 2009 Elsevier Inc. All rights reserved.
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