Full Text:   <2921>

CLC number: Q756;TP181

On-line Access: 2024-08-27

Received: 2023-10-17

Revision Accepted: 2024-05-08

Crosschecked: 0000-00-00

Cited: 0

Clicked: 6628

Citations:  Bibtex RefMan EndNote GB/T7714

-   Go to

Article info.
Open peer comments

Journal of Zhejiang University SCIENCE A 2003 Vol.4 No.5 P.573-577

http://doi.org/10.1631/jzus.2003.0573


Splicing-site recognition of rice (Oryza sativa L.)DNA sequences by support vector machines


Author(s):  PENG Si-hua, FAN Long-jiang, PENG Xiao-ning, ZHUANG Shu-lin, DU Wei, CHEN Liang-biao

Affiliation(s):  Department of Control Science and Engineering, College of Information Science and Engineering,Zhejiang University, Hangzhou 310027, China; more

Corresponding email(s):   pengsihua@zju.edu.cn, liangbiao@zju.edu.cn

Key Words:  Support vector machines, Machine learning, Intron, Splicing site, Oryza sativa


Share this article to: More


Abstract: 
Motivation: It was found that high accuracy splicing-site recognition of rice (Oryza sativa L.) DNA sequence is especially difficult. We described a new method for the splicing-site recognition of rice DNA sequences. Method: Based on the intron in eukaryotic organisms conforming to the principle of GT-AG, we used support vector machines (SVM) to predict the splicing sites. By machine learning, we built a model and used it to test the effect of the test data set of true and pseudo splicing sites. Results: The prediction accuracy we obtained was 87.53% at the true 5' end splicing site and 87.37% at the true 3' end splicing sites. The results suggested that the SVM approach could achieve higher accuracy than the previous approaches.

Open peer comments: Debate/Discuss/Question/Opinion

<1>

Please provide your name, email address and a comment





Journal of Zhejiang University-SCIENCE, 38 Zheda Road, Hangzhou 310027, China
Tel: +86-571-87952783; E-mail: cjzhang@zju.edu.cn
Copyright © 2000 - 2025 Journal of Zhejiang University-SCIENCE