Splice site detection in DNA sequences using a fast classification algorithm

Authors:
Jair Cervantes;Xiaoou Li;Wen Yu
Affiliations:
Department of Computer Science, CINVESTAV, México D.F., México;Department of Computer Science, CINVESTAV, México D.F., México;Department of Automatic Control, CINVESTAV, México D.F., México
Venue:
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
Year:
2009

Citing 6
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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.