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Gene Classification Using Codon Usage and Support Vector Machines

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3 Author(s)
Jianmin Ma ; Biolnf. Res. Center, Nanyang Technol. Univ., Singapore ; Nguyen, M.N. ; Rajapakse, J.C.

A novel approach for gene classification, which adopts codon usage bias as input feature vector for classification by support vector machines (SVM) is proposed. The DNA sequence is first converted to a 59-dimensional feature vector where each element corresponds to the relative synonymous usage frequency of a codon. As the input to the classifier is independent of sequence length and variance, our approach is useful when the sequences to be classified are of different lengths, a condition that homology-based methods tend to fail. The method is demonstrated by using 1,841 Human Leukocyte Antigen (HLA) sequences which are classified into two major classes: HLA-I and HLA-II; each major class is further subdivided into sub-groups of HLA-I and HLA-II molecules. Using codon usage frequencies, binary SVM achieved accuracy rate of 99.3% for HLA major class classification and multi-class SVM achieved accuracy rates of 99.73% and 98.38% for sub-class classification of HLA-I and HLA-II molecules, respectively. The results show that gene classification based on codon usage bias is consistent with the molecular structures and biological functions of HLA molecules.

Published in:

Computational Biology and Bioinformatics, IEEE/ACM Transactions on  (Volume:6 ,  Issue: 1 )

Date of Publication:

Jan.-March 2009

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