DocumentCode :
2785408
Title :
Research of protein structure classification based on rough set and support vector machine
Author :
Jian, Wang ; Jian-Ping, Li
Author_Institution :
Sch. of Comput. & Inf. Sci., Neijiang Normal Univ., Neijiang, China
fYear :
2009
fDate :
23-25 Oct. 2009
Firstpage :
124
Lastpage :
127
Abstract :
A novel method of feature extraction form protein sequences, structures and physicochemical properties has been proposed and obtained a better classification results by the key eigenvector obtained form knowledge reduction combined with the algorithm of support vector machine. Based on Jackknife detecting methods, the comprehensive classification results 78.3% and 90.9% for all-¿, all-ß, ¿+ß and ¿/ß have been obtained by the method of support vector machine when we tested Z277 and Z498 in database. Moreover, we found that protein physicochemical properties have a strong influence on classification precision of protein structure with Matlab. These results show that the method of feature extraction based on rough set is effective and available, the research of protein structure for support vector machine classification based on rough set is very effective.
Keywords :
biology computing; eigenvalues and eigenfunctions; feature extraction; pattern classification; proteins; rough set theory; sequences; support vector machines; Jackknife detecting methods; Matlab; classification precision; feature extraction; key eigenvector; knowledge reduction; physicochemical property; protein sequences; protein structure classification; rough set; support vector machine; Amino acids; Feature extraction; Information science; Physics computing; Protein engineering; Protein sequence; Spatial databases; Support vector machine classification; Support vector machines; Testing; Classification of protein structure; Feature extraction; Rough set; Support vector machines;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Apperceiving Computing and Intelligence Analysis, 2009. ICACIA 2009. International Conference on
Conference_Location :
Chengdu
Print_ISBN :
978-1-4244-5204-0
Electronic_ISBN :
978-1-4244-5206-4
Type :
conf
DOI :
10.1109/ICACIA.2009.5361135
Filename :
5361135
Link To Document :
بازگشت