Operon Prediction by Decision Tree Classifier Based on VPRSM

摘要：

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.

关键词： VPRSM intergenic distance COG functions Gene order conservation

作者: Shuqin Wang Shuqin Wang Fangxun Sun Yingsi Wu Wei Du Chunguang Zhou Yanchun Liang

作者单位: School of Mathematics & Statistics,Northeast Normal University,Changchun,130024,China College of Computer Science and Technology,Jilin University,Changchun,130012,China

会议类型: 国际会议

会议名称: The 3rd International Conference on Bioinformatics and Biomedical Engineering(iCBBE 2009)(第三届生物信息与生物医学工程国际会议)

会议地点: 北京

会议语种:英文

页码: 1-4

在线出版日期: 2009-06-11（万方平台首次上网日期，不代表论文的发表时间）