Combining three scoring algorithms for representing protein sequence

Nazar Zaki, Safaai Deris

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Effective representation of the protein sequence is a key issue in detecting remote protein homology. Recent work using string kernels for protein data has achieved state-of-the-art performance for protein classification. However, such representations are suffering from high dimensionality problem. In this work, we introduce a simple method based on representing the protein sequence by fix dimensions of the length three. We present hidden Markov model combining scores method. Three scoring algorithms are combined to represent protein sequence of amino acids for better remote homology detection. We tested the method on the SCOP version 1.37 dataset. The results show that, with such a simple representation, we are able to achieve superior performance to previously presented protein homology detection methods while achieving better computational efficiency.

Original languageEnglish
Title of host publicationProceedings of the 2005 International Conference on Artificial Intelligence, ICAI'05
Pages839-845
Number of pages7
Publication statusPublished - 2005
Event2005 International Conference on Artificial Intelligence, ICAI'05 - Las Vegas, NV, United States
Duration: Jun 27 2005Jun 30 2005

Publication series

NameProceedings of the 2005 International Conference on Artificial Intelligence, ICAI'05
Volume2

Other

Other2005 International Conference on Artificial Intelligence, ICAI'05
Country/TerritoryUnited States
CityLas Vegas, NV
Period6/27/056/30/05

ASJC Scopus subject areas

  • Artificial Intelligence

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