Skip Navigation


Bioinformatics Advance Access originally published online on February 15, 2006
Bioinformatics 2006 22(10):1207-1210; doi:10.1093/bioinformatics/btl055
This Article
Right arrow Full Text Freely available
Right arrow FREE Full Text (Print PDF) Freely available
Right arrow All Versions of this Article:
22/10/1207    most recent
btl055v1
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Similar articles in ISI Web of Science
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Add to My Personal Archive
Right arrow Download to citation manager
Right arrow Search for citing articles in:
ISI Web of Science (8)
Right arrowRequest Permissions
Google Scholar
Right arrow Articles by Nanni, L.
Right arrow Articles by Lumini, A.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Nanni, L.
Right arrow Articles by Lumini, A.
Social Bookmarking
Add to CiteULike   Add to Connotea   Add to Del.icio.us  
What's this?

© The Author 2006. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

An ensemble of K-local hyperplanes for predicting protein–protein interactions

Loris Nanni and Alessandra Lumini

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

Add to CiteULike CiteULike   Add to Connotea Connotea   Add to Del.icio.us Del.icio.us    What's this?


This article has been cited by other articles:


Home page
 BioinformaticsHome page
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]

Home page
 BioinformaticsHome page
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]


Disclaimer:
Please note that abstracts for content published before 1996 were created through digital scanning and may therefore not exactly replicate the text of the original print issues. All efforts have been made to ensure accuracy, but the Publisher will not be held responsible for any remaining inaccuracies. If you require any further clarification, please contact our Customer Services Department.