Bioinformatics Advance Access originally published online on February 15, 2006
Bioinformatics 2006 22(10):1207-1210; doi:10.1093/bioinformatics/btl055
| ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
An ensemble of K-local hyperplanes for predicting protein–protein interactions
DEIS, IEIIT, CNR, Università di Bologna Viale Risorgimento 2, 40136 Bologna, Italy
*To whom correspondence should be addressed.
Prediction of protein–protein interaction is a difficult and important problem in biology. In this paper, we propose a new method based on an ensemble of K-local hyperplane distance nearest neighbor (HKNN) classifiers, where each HKNN is trained using a different physicochemical property of the amino acids. Moreover, we propose a new encoding technique that combines the amino acid indices together with the 2-Grams amino acid composition. A fusion of HKNN classifiers combined with the ‘Sum rule’ enables us to obtain an improvement over other state-of-the-art methods. The approach is demonstrated by building a learning system based on experimentally validated protein–protein interactions in human gastric bacterium Helicobacter pylori and in Human dataset.
Contact: lnanni{at}deis.unibo.it
Received on December 5, 2005; revised on January 30, 2006; accepted on February 10, 2006
CiteULike 
Connotea 
Del.icio.us What's this?
This article has been cited by other articles:
|
|
 |
|
  C.-W. Tung and S.-Y. Ho POPI: predicting immunogenicity of MHC class I binding peptides by mining informative physicochemical properties Bioinformatics, April 15, 2007; 23(8): 942 - 949. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
E. Ferraro, A. Via, G. Ausiello, and M. Helmer-Citterich A novel structure-based encoding for machine-learning applied to the inference of SH3 domain specificity Bioinformatics, October 1, 2006; 22(19): 2333 - 2339. [Abstract] [Full Text] [PDF]
|
|