Journal
SCIENCEASIA
Volume 39, Issue 1, Pages 42-49Publisher
SCIENCE SOCIETY THAILAND
DOI: 10.2306/scienceasia1513-1874.2013.39.042
Keywords
gene classification; machine learning; computational method
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Funding
- Thailand Research Fund through the Royal Golden Jubilee Ph.D. Programme [PHD/0209/2552]
- Chiang Mai University's Graduate School
- National Centre for Genetic Engineering and Biotechnology, Thailand
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Human Leukocyte Antigen (HLA) plays an important role in the control of self-recognition including defence against microorganisms. The efficient performance of classifying HLA genes facilitates the understanding of the BELA and immune systems. Currently, the classification of BLA genes has been developed by using various computational methods based on codon and di-codon usages. Here, we directly classify the BLA genes by using the k-nearest neighbour (k-NN) classifier. To develop an efficient k-NN classifier, we propose the use of a spectrum kernel to investigate HLA genes. Our approach achieves an accuracy as high as 99.4% of the HLA major classes prediction measured by ten-fold cross-validation. Moreover, we give a maximum accuracy of 99.4% in the HLA-I subclasses. These results show that our proposed method is relatively simple and can give higher accuracies than other sophisticated and conventional methods.
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