iACP: a sequence-based tool for identifying anticancer peptides
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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
Hao Lin, e-mail: firstname.lastname@example.org
Kuo-Chen Chou, e-mail: email@example.com
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, by which users can easily obtain their desired results.
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