Skip Navigation


Bioinformatics Advance Access originally published online on December 14, 2004
Bioinformatics 2005 21(8):1429-1436; doi:10.1093/bioinformatics/bti212
This Article
Right arrow Full Text Freely available
Right arrow FREE Full Text (Print PDF) Freely available
Right arrow All Versions of this Article:
21/8/1429    most recent
bti212v1
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 (10)
Right arrowRequest Permissions
Google Scholar
Right arrow Articles by Atalay, V.
Right arrow Articles by Cetin-Atalay, R.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Atalay, V.
Right arrow Articles by Cetin-Atalay, R.
Social Bookmarking
 Add to CiteULike   Add to Connotea   Add to Del.icio.us  
What's this?

© The Author 2004. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions{at}oupjournals.org

Implicit motif distribution based hybrid computational kernel for sequence classification

Volkan Atalay 1,2 and Rengul Cetin-Atalay 1,3,*

1Virginia Bioinformatics Institute, Virginia Polytechnic Institute and State University Blacksburg VA 24061, USA
2Department of Computer Engineering, Middle East Technical University TR-06531 Ankara, Turkey
3Department of Molecular Biology and Genetics, Faculty of Science, Bilkent University 06533 Ankara, Turkey

*To whom correspondence should be addressed.

Motivation: We designed a general computational kernel for classification problems that require specific motif extraction and search from sequences. Instead of searching for explicit motifs, our approach finds the distribution of implicit motifs and uses as a feature for classification. Implicit motif distribution approach may be used as modus operandi for bioinformatics problems that require specific motif extraction and search, which is otherwise computationally prohibitive.

Results: A system named P2SL that infer protein subcellular targeting was developed through this computational kernel. Targeting-signal was modeled by the distribution of subsequence occurrences (implicit motifs) using self-organizing maps. The boundaries among the classes were then determined with a set of support vector machines. P2SL hybrid computational system achieved ~81% of prediction accuracy rate over ER targeted, cytosolic, mitochondrial and nuclear protein localization classes. P2SL additionally offers the distribution potential of proteins among localization classes, which is particularly important for proteins, shuttle between nucleus and cytosol.

Availability: http://staff.vbi.vt.edu/volkan/p2sl and http://www.i-cancer.fen.bilkent.edu.tr/p2sl

Contact: rengul{at}bilkent.edu.tr


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
D. A. R. S. Latino, Q.-Y. Zhang, and J. Aires-de-Sousa
Genome-scale classification of metabolic reactions and assignment of EC numbers with self-organizing maps
Bioinformatics, October 1, 2008; 24(19): 2236 - 2244.
[Abstract] [Full Text] [PDF]


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]



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.