Splice site detection in DNA sequences using a fast classification algorithm

Author

Cervantes, Jair ; Li, XiaoOu ; Yu, Wen

Author_Institution

Dept. of Comput. Sci., CINVESTAV, Mexico City, Mexico

fYear

2009

fDate

11-14 Oct. 2009

Firstpage

2683

Lastpage

2688

Abstract

Support vector machines (SVMs) are known to be excellent algorithms for classification problems. The principal disadvantage of SVMs is due to its excessive training time in large data set, such as DNA sequences. This paper presents a novel SVMs classification method which reduces significantly the input data set using Bayesian technique. Using this system, we are able to predict with a high accuracy huge data sets in a reasonable time. The system has been tested successfully on large splice-junction gene sequences (DNA). Experimental results show that the accuracy obtained by the proposed algorithm is comparable (98.2) with other SVMs implementations such as SMO (98.4%), LibSVM (98.4%), and Simple SVM (97.6%). Furthermore the proposed approach is scalable to large data sets with high classification accuracy.

Keywords

Bayes methods; bioinformatics; support vector machines; Bayesian technique; DNA sequences; SVM; fast classification algorithm; large data sets; splice site detection; splice-junction gene sequences; support vector machines; Bayesian methods; Bioinformatics; Biological information theory; Classification algorithms; DNA; Proteins; Sequences; Splicing; Support vector machine classification; Support vector machines; Bayesian classification; DNA; Large data sets; SVM; Splice sites detection;

fLanguage

English

Publisher

ieee

Conference_Titel

Systems, Man and Cybernetics, 2009. SMC 2009. IEEE International Conference on

Conference_Location

San Antonio, TX

ISSN

1062-922X

Print_ISBN

978-1-4244-2793-2

Electronic_ISBN

1062-922X

Type

conf

DOI

10.1109/ICSMC.2009.5346130

Filename

5346130