A Novel Model-based Method for Feature Extraction from Protein Sequences for Classification

Author

Saraç, Ömer Sinan ; Atalay, Volkan ; Atalay, Rengül Çetin

Author_Institution

Bilgisayar Muhendisligi Bolumu, Orta Dogu Teknik Univ., Ankara

fYear

2006

fDate

17-19 April 2006

Firstpage

1

Lastpage

4

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

Keywords

data encapsulation; expectation-maximisation algorithm; hidden Markov models; image classification; image representation; image sequences; molecular biophysics; pattern clustering; proteins; HMM mixture model; amino-acid sequence representation; expectation-maximization algorithm; feature extraction; hidden Markov model; information hiding; protein classification; subsequence clustering; Expectation-maximization algorithms; Feature extraction; Hidden Markov models; Influenza; Proteins;

fLanguage

English

Publisher

ieee

Conference_Titel

Signal Processing and Communications Applications, 2006 IEEE 14th

Conference_Location

Antalya

Print_ISBN

1-4244-0238-7

Type

conf

DOI

10.1109/SIU.2006.1659859

Filename

1659859