Effective statistical features for coding and non-coding DNA sequence classification for yeast, C. elegans and human

Authors:
Alan Wee-/Chung Liew;Yonghui Wu;Hong Yan;Mengsu Yang
Affiliations:
Department of Computer Science and Engineering, The Chinese University of Hong Kong, Shatin, Hong Kong.;Agenica Research Pte Ltd, 11 Hospital Drive, 169610, Singapore.;Department of Computer Engineering and Information Technology, City University of Hong Kong, Kowloon, Hong Kong/ School of Electrical and Information Engineering, University of Sydney, NSW 2006, A ...;Department of Chemistry and Biology, City University of Hong Kong, Kowloon, Hong Kong
Venue:
International Journal of Bioinformatics Research and Applications
Year:
2005

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Abstract

This study performs a quantitative evaluation of the different coding features in terms of their information content for the classification of coding and non-coding regions for three species. Our study indicated that coding features that are effective for yeast or C. elegans are generally not very effective for human, which has a short average exon length. By performing a correlation analysis, we identified a subset of human coding features with high discriminative power, but complementary in their information content. For this subset, a classification accuracy of up to 90% was obtained using a simple kNN classifier.