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Journal of Biological Sciences
  Year: 2005 | Volume: 5 | Issue: 6 | Page No.: 795-800
DOI: 10.3923/jbs.2005.795.800
Representing Protein Sequence with Low Number of Dimensions
Nazar Zaki and Safaai Deris

Abstract:
This study introduces a simple method based on representing 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.
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How to cite this article:

Nazar Zaki and Safaai Deris , 2005. Representing Protein Sequence with Low Number of Dimensions. Journal of Biological Sciences, 5: 795-800.

DOI: 10.3923/jbs.2005.795.800

URL: https://scialert.net/abstract/?doi=jbs.2005.795.800

 
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