Skip Navigation



Bioinformatics Advance Access published online on September 27, 2005
Bioinformatics, doi:10.1093/bioinformatics/bti687
This Article
Right arrow Advance Access manuscript (PDF) Freely available
Right arrowOA All Versions of this Article:
21/23/4239    most recent
bti687v1
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 PubMed
Right arrow Alert me to new issues of the journal
Right arrow Add to My Personal Archive
Right arrow Download to citation manager
Google Scholar
Right arrow Articles by Rangwala, H.
Right arrow Articles by Karypis, G.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Rangwala, H.
Right arrow Articles by Karypis, G.
Social Bookmarking
Add to CiteULike   Add to Connotea   Add to Del.icio.us  
What's this?

© The Author (2005). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org
Received May 12, 2005
Revised June 15, 2005
Accepted September 20, 2005

Article

Profile based direct kernels for remote homology detection and fold recognition

Huzefa Rangwala 1 and George Karypis 1*

1 Department of Computer Science & Engineering, University of Minnesota, Minneapolis, MN 55455

* To whom correspondence should be addressed.
George Karypis, E-mail: karypis{at}cs.umn.edu


  Abstract

Motivation: Protein remote homology detection is a central problem in computational biology. Supervised learning algorithms based on support vector machines are currently one of the most effective methods for remote homology detection. The performance of these methods depends on how the protein sequences are modeled and on the method used to compute the kernel function between them.

Results: We introduce two classes of kernel functions that are constructed by combining sequence profiles with new and existing approaches for determining the similarity between pairs of protein sequences. These kernels are constructed directly from these explicit protein similarity measures and employ effective profile-to-profile scoring schemes for measuring the similarity between pairs of proteins. Experiments with remote homology detection and fold recognition problems show that these kernels are capable of producing results that are substantially better than those produced by all of the existing state-of-the-art SVM-based methods. In addition, the experiments show that these kernels, even when used in the absence of profiles, produce results that are better than those produced by existing non-profile-based schemes.

Availability: The programs for computing the various kernel functions are available on request from the authors.


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
A. R. Shah, C. S. Oehmen, and B.-J. Webb-Robertson
SVM-HUSTLE--an iterative semi-supervised machine learning approach for pairwise protein remote homology detection
Bioinformatics, March 15, 2008; 24(6): 783 - 790.
[Abstract] [Full Text] [PDF]

Home page
 BioinformaticsHome page
S. Hochreiter, M. Heusel, and K. Obermayer
Fast model-based protein homology detection without alignment
Bioinformatics, July 15, 2007; 23(14): 1728 - 1736.
[Abstract] [Full Text] [PDF]

Home page
 Brief BioinformHome page
P. Fariselli, I. Rossi, E. Capriotti, and R. Casadio
The WWWH of remote homolog detection: The state of the art
Brief Bioinform, March 1, 2007; 8(2): 78 - 87.
[Abstract] [Full Text] [PDF]

Home page
 BioinformaticsHome page
H. Rangwala and G. Karypis
Incremental window-based protein sequence alignment algorithms
Bioinformatics, January 15, 2007; 23(2): e17 - e23.
[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.