Bioinformatics Advance Access published online on December 14, 2004
Bioinformatics, doi:10.1093/bioinformatics/bti212
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1 Virginia Bioinformatics Institute, Virginia Polytechnic Institute and State University Blacksburg, VA 24061, USA; Department of Computer Engineering, Middle East Technical University, TR-06531 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 requires 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 Availability: http://staff.vbi.vt.edu/volkan/p2sl.
Received September 24, 2004
Revised December 6, 2004
Accepted December 7, 2004
Article
Implicit motif distribution based hybrid computational kernel for sequence classification
2 Virginia Bioinformatics Institute, Virginia Polytechnic Institute and State University Blacksburg, VA 24061, USA; Department of Molecular Biology and Genetics, Faculty of Science, Bilkent University, 06533 Ankara, Turkey
Rengul Cetin-Atalay, E-mail: rengul{at}bilkent.edu.tr
Abstract 
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.
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