Research Papers:

iACP: a sequence-based tool for identifying anticancer peptides

Wei Chen _, Hui Ding, Pengmian Feng, Hao Lin and Kuo-Chen Chou

PDF  |  HTML  |  Supplementary Files  |  How to cite  |  Order a Reprint

Oncotarget. 2016; 7:16895-16909. https://doi.org/10.18632/oncotarget.7815

Metrics: PDF 1701 views  |   HTML 2050 views  |   ?  


Wei Chen1,4, Hui Ding2, Pengmian Feng3, Hao Lin2,4, Kuo-Chen Chou4,5

1Department of Physics, School of Sciences, Center for Genomics and Computational Biology, North China University of Science and Technology, Tangshan, China

2Key Laboratory for Neuro-Information of Ministry of Education, Center of Bioinformatics, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China

3School of Public Health, North China University of Science and Technology, Tangshan, China

4Gordon Life Science Institute, Belmont, Massachusetts, United States of America

5Center of Excellence in Genomic Medicine Research (CEGMR), King Abdulaziz University, Jeddah, Saudi Arabia

Correspondence to:

Wei Chen, e-mail: wchen@gordonlifescience.org, chenweiimu@gmail.com

Hao Lin, e-mail: hlin@uestc.edu.cn

Kuo-Chen Chou, e-mail: kcchou@gordonlifescience.org

Keywords: anticancer peptides, PseAAC, g-gap dipeptide mode, incremental feature selection, iACP webserver

Received: January 06, 2016     Accepted: February 11, 2016     Published: March 01, 2016


Cancer remains a major killer worldwide. Traditional methods of cancer treatment are expensive and have some deleterious side effects on normal cells. Fortunately, the discovery of anticancer peptides (ACPs) has paved a new way for cancer treatment. With the explosive growth of peptide sequences generated in the post genomic age, it is highly desired to develop computational methods for rapidly and effectively identifying ACPs, so as to speed up their application in treating cancer. Here we report a sequence-based predictor called iACP developed by the approach of optimizing the g-gap dipeptide components. It was demonstrated by rigorous cross-validations that the new predictor remarkably outperformed the existing predictors for the same purpose in both overall accuracy and stability. For the convenience of most experimental scientists, a publicly accessible web-server for iACP has been established at http://lin.uestc.edu.cn/server/iACP, by which users can easily obtain their desired results.

Creative Commons License All site content, except where otherwise noted, is licensed under a Creative Commons Attribution 3.0 License.
PII: 7815