Operon Prediction by Decision Tree Classifier Based on VPRSM

Author

Wang, Shuqin ; Sun, Fangxun ; Wu, Yingsi ; Du, Wei ; Zhou, Chunguang ; Liang, Yanchun

fYear

2009

fDate

11-13 June 2009

Firstpage

1

Lastpage

4

Abstract

The prediction of operons is critical to reconstruction of regulatory networks at the whole genome level. In this paper, a novel approach based on variable precision rough set model (VPRSM) is presented to prediction of operon. We use five effective features: max distance, min distance, gene strand direction information, scores of COG, and scores of gene order conservation. The proposed method is examined on Escherichia coli K12 and an accuracy of 89.4% is obtained. We also compare this method with C4.5 and BP. The results indicate that VPRSM based decision tree classifier is an effective classifier for predicting operon.

Keywords

biology computing; decision trees; genetics; genomics; microorganisms; pattern classification; prediction theory; rough set theory; Escherichia coli K12; decision tree classifier; gene order conservation; gene strand direction information; genome level; operon prediction; regulatory networks; variable precision rough set model; Bioinformatics; Biological processes; Biology computing; Classification tree analysis; Decision trees; Genomics; Mathematics; Predictive models; Spatial databases; Statistics;

fLanguage

English

Publisher

ieee

Conference_Titel

Bioinformatics and Biomedical Engineering , 2009. ICBBE 2009. 3rd International Conference on

Conference_Location

Beijing

Print_ISBN

978-1-4244-2901-1

Electronic_ISBN

978-1-4244-2902-8

Type

conf

DOI

10.1109/ICBBE.2009.5163147

Filename

5163147