Effective Statistical Features for Coding and Non-coding DNA Sequence Classification for Yeast, C. elegans and Human

Liew, AWC; Wu, Y; Yan, H; Yan, H; Yang, M

doi:10.1504/IJBRA.2005.007577

Author(s)

Liew, AWC

Wu, Y

Yan, H

Yang, M

Griffith University Author(s)

Liew, Alan Wee-Chung

Year published

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.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.
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