ON NETWORK-BASED KERNEL METHODS FOR PROTEIN-PROTEIN INTERACTIONS WITH APPLICATIONS IN PROTEIN FUNCTIONS PREDICTION

Predicting protein functions is an important issue in the post-genomic era. This paper studies several network-based kernels including local linear embedding （LLE） kernel method, diffusion kernel and laplacian kernel to uncover the relationship between proteins functions and protein-protein interact...

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Bibliographic Details
Published in:Journal of systems science and complexity 2010-10, Vol.23 (5), p.917-930
Main Authors: Li, Limin, Ching, Waiki, Chan, Yatming, Mamitsuka, Hiroshi
Format: Article
Language:English
Subjects:
Complex Systems
Control
Laplacian
Mathematics
Mathematics and Statistics
Mathematics of Computing
Operations Research/Decision Theory
Statistics
Systems Theory
交叉验证
后基因组时代
局部线性嵌入
蛋白质功能预测
蛋白质相互作用网络
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Summary:Predicting protein functions is an important issue in the post-genomic era. This paper studies several network-based kernels including local linear embedding （LLE） kernel method, diffusion kernel and laplacian kernel to uncover the relationship between proteins functions and protein-protein interactions （PPI）. The author first construct kernels based on PPI networks, then apply support vector machine （SVM） techniques to classify proteins into different functional groups. The 5-fold cross validation is then applied to the selected 359 GO terms to compare the performance of different kernels and guilt-by-association methods including neighbor counting methods and Chi-square methods. Finally, the authors conduct predictions of functions of some unknown genes and verify the preciseness of our prediction in part by the information of other data source.
ISSN:1009-6124
1559-7067
DOI:10.1007/s11424-010-0207-y