Sequence-based protein-protein interaction prediction via support vector machine
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This paper develops sequence-based methods for identifying novel protein-protein interactions (PPIs) by means of support vector machines (SVMs). The authors encode proteins ont only in the gene level but also in the amino acid level, and design a procedure to select negative training set for dealing with the training dataset imbalance problem, i.e., the number of interacting protein pairs is scarce relative to large scale non-interacting protein pairs. The proposed methods are validated on PPIs data of Plasmodium falciparum and Escherichia coli, and yields the predictive accuracy of 93.8% and 95.3%, respectively. The functional annotation analysis and database search indicate that our novel predictions are worthy of future experimental validation. The new methods will be useful supplementary tools for the future proteomics studies.
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- Sequence-based protein-protein interaction prediction via support vector machine
Journal of Systems Science and Complexity
Volume 23, Issue 5 , pp 1012-1023
- Cover Date
- Print ISSN
- Online ISSN
- Additional Links
- Imbalance problem
- protein-protein interactions
- support vector machine
- Author Affiliations
- 1. College of Science, China Agricultural University, Beijing, 100083, China
- 2. Key Laboratory of Adaptation and Evolution of Plateau Biota, Northwest Institute of Plateau Biology, Chinese Academy of Sciences, Xining, 810008, China
- 3. Institute of Systems Science, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, 100190, China
- 4. College of Mathematics and Systems Science, Xinjiang University, Urumuchi, 830046, China