Effective statistical features for coding and non-coding DNA sequence classification for yeast, C. elegans and human.
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.
Alan Wee-Chung
AW
Liew
Yonghui
Y
Wu
Hong
H
Yan
Mengsu
M
Yang
D000818:Animals; D001483:Base Sequence; D017173:Caenorhabditis elegans; D005091:Exons; D006801:Humans; D016366:Open Reading Frames; D017422:Sequence Analysis, DNA