DNA information mining based on Hidden Markov Models

Author

Luo, Zeju ; Song, Lihong

Author_Institution

Res. Center of the Econ. of the Upper Reaches of Yangtze River, Chong Qing Technol. & Bus. Univ., Chongqing, China

Volume

1

fYear

2010

fDate

10-12 Aug. 2010

Firstpage

238

Lastpage

241

Abstract

Use the characteristics that different structures of the protein sequence has the different distribution of its information in the Hidden Markov Model training, classify different family of proteins sequence according to different mapping information,so as to to identify the different family of proteins. Experimental results show that the average recognition rate reach 92.8%. Recognition results show that the computing time of Hidden Markov Models is not only less than the support vector machine in multi-classification problem, but also the recognition rate is higher than support vector machine, show that the special advantages of Hidden Markov Model in dealing with multi-class DNA information mining.

Keywords

DNA; biology computing; data mining; hidden Markov models; proteins; support vector machines; DNA information mining; hidden Markov models; multi-classification problem; protein sequence; support vector machine; Biological system modeling; DNA; Hidden Markov models; Markov processes; Protein sequence; Support vector machines; DNA coding; Hidden Markov models; multi-classification; protein sequence identification;

fLanguage

English

Publisher

ieee

Conference_Titel

Natural Computation (ICNC), 2010 Sixth International Conference on

Conference_Location

Yantai, Shandong

Print_ISBN

978-1-4244-5958-2

Type

conf

DOI

10.1109/ICNC.2010.5582898

Filename

5582898