A novel model-based method for feature extraction from protein sequences for classification

Files

A novel model-based method for feature extraction from protein sequences for classification [Siniflandirma için protein dizilerinin özniteliklerinin çikarilmasinda model tabanli yeni bir yöntem].pdf (1.44 MB)

Date

2006

Authors

Editor(s)

Advisor

Supervisor

Co-Advisor

Co-Supervisor

Instructor

Source Title

Proceedings of the IEEE 14th Signal Processing and Communications Applications Conference, SIU 2006

Print ISSN

Electronic ISSN

Publisher

IEEE

Volume

2006

Issue

Pages

Language

Turkish

Type

Conference Paper

Journal Title

Journal ISSN

Volume Title

Citation Stats
Attention Stats
Usage Stats
2
views
18
downloads

Series

Abstract

Representation of amino-acid sequences constitutes the key point in classification of proteins into functional or structural classes. The representation should contain the biologically meaningful information hidden in the primary sequence of the protein. Conserved or similar subsequences are strong indicators of functional and structural similarity. In this study we present a feature mapping that takes into account the models of the subsequences of protein sequences. An expectation-maximization algorithm along with an HMM mixture model is used to cluster and learn the models of subsequences of a given set of proteins.

Course

Other identifiers

Book Title

Keywords

Primary sequences, Protein sequences, Structural classes, Algorithms, Amino acids, Classification (of information), Cluster analysis, Hidden Markov models, Optimization, Proteins, Feature extraction

Degree Discipline

Degree Level

Degree Name

Citation

Permalink

http://hdl.handle.net/11693/27176

Published Version (Please cite this version)

http://dx.doi.org/10.1109/SIU.2006.1659859

Collections

Scholarly Publications - Molecular Biology and Genetics