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iACP: a sequence-based tool for identifying anticancer peptides

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Chen, Wei, Ding, Hui, Feng, Pengmian, Lin, Hao, Chou, Kuo-Chen·2016·Oncotarget
DOI 10.18632/oncotarget.7815
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Abstract

// Wei Chen 1, 4 , Hui Ding 2 , Pengmian Feng 3 , Hao Lin 2, 4 , Kuo-Chen Chou 4, 5 1 Department of Physics, School of Sciences, Center for Genomics and Computational Biology, North China University of Science and Technology, Tangshan, China 2 Key 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 3 School of Public Health, North China University of Science and Technology, Tangshan, China 4 Gordon Life Science Institute, Belmont, Massachusetts, United States of America 5 Center 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 ABSTRACT 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.

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Cited by

See all 433

Metadata

Authors
Chen, W., Ding, H., Feng, P., Lin, H., & Chou, K.-C.
Venue
Oncotarget
Publisher
Impact Journals, LLC
Published
2016-03-01
Volume
7
Issue
13
Pages
16895-16909
ISSN
1949-2553 (ISSN-L)
DOI
10.18632/oncotarget.7815
OpenAlex
W2340970647
Cited by
433
References
8
FWCI
29.34
Type
journal article
Language
en
Open access
diamond

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

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How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: iACP: a sequence-based tool for identifying anticancer peptides. https://app.cochat.ai/papers/10.18632/oncotarget.7815
The paper
Chen, W., Ding, H., Feng, P., Lin, H., & Chou, K.-C. (2016). iACP: a sequence-based tool for identifying anticancer peptides. Oncotarget. https://doi.org/10.18632/oncotarget.7815

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.