Paper Info
Title
Combining Three Scoring Algorithms for Representing Protein Sequence
Abstract
Effective representation of 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 representation 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 and 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.
Year
Venue
Keywords
2005
ICAI '05: PROCEEDINGS OF THE 2005 INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE, VOLS 1 AND 2
amino acid,protein sequence,string kernel,hidden markov model
Field
DocType
Citations 
Protein sequencing,Pattern recognition,Computer science,Algorithm,Curse of dimensionality,Homology (biology),Artificial intelligence,Hidden Markov model
Conference
0
PageRank 
References 
Authors
0.34
9
2
Name
Order
Citations
PageRank
Nazar Zaki113914.31
Safaai Deris225642.99