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

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Title Effective Statistical Features for Coding and Non-coding DNA Sequence Classification for Yeast, C. elegans and Human
Author Liew, Alan Wee-Chung; Wu, Yonghui; Yan, Hong; Yang, Mengsu
Journal Name International Journal of Bioinformatics Research and Applications
Year Published 2005
Place of publication United Kingdom
Publisher Inderscience Publishers
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.
Peer Reviewed Yes
Published Yes
Publisher URI http://www.inderscience.com/ijbra
Alternative URI http://dx.doi.org/0.1504/IJBRA.2005.007577
Copyright Statement Copyright 2005 Inderscience Publishers. Please refer to the journal's website for access to the definitive, published version.
Volume 1
Issue Number 2
Page from 181
Page to 201
ISSN 1744-5485
Date Accessioned 2007-06-08
Language en_AU
Research Centre Institute for Integrated and Intelligent Systems
Faculty Faculty of Science, Environment, Engineering and Technology
Subject PRE2009-Gene Expression; PRE2009-Pattern Recognition
URI http://hdl.handle.net/10072/22081
Publication Type Journal Articles (Refereed Article)
Publication Type Code c1x

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