Hidden Markov model used in protein sequence analysis.
- Author:
Xiaoming WU
1
;
Changxin SONG
;
Bo WANG
;
Jingzhi CHENG
Author Information
1. Bioinformatics Research Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an 710049.
- Publication Type:Journal Article
- MeSH:
Algorithms;
Linear Models;
Proteins;
classification;
Sequence Analysis, Protein;
methods
- From:
Journal of Biomedical Engineering
2002;19(3):455-458
- CountryChina
- Language:Chinese
-
Abstract:
Hidden Markov model (HMM) used in the research of protein is a new field of bioinformatics. Nowadays large amount of data about protein sequences and structures have been obtained. Traditional methods of protein analysis are no longer used. Biologists have updated their research methods with computer technology and statistics, which can deal with large amount of data. HMM can be used to distinguish protein sequence with the same characteristics. A family of protein from SCOP database was selected, through which a HMM model representing the family was obtained, and then the model was utilized to analyze protein sequences. Results indicate that HMM can express particular family of protein, and recognize the given protein sequences of the family from many sequences.
