ACPred-FL: a sequence-based predictor using effective feature representation to improve the prediction of anti-cancer peptides-Reference-Cited by-同舟云学术

ACPred-FL: a sequence-based predictor using effective feature representation to improve the prediction of anti-cancer peptides

Published:2018-06-01 Issue: Volume: Page:
ISSN:1367-4803
Container-title:Bioinformatics
language:en
Short-container-title:

Author:

Wei Leyi¹²^ORCID,Zhou Chen¹,Chen Huangrong¹,Song Jiangning³⁴^ORCID,Su Ran⁵²

Affiliation:

1. School of Computer Science and Technology, Tianjin University, Tianjin, China

2. State Key Laboratory of Medicinal Chemical Biology, Nankai University, Tianjin, China

3. Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology

4. Monash Centre for Data Science, Faculty of Information Technology, Monash University, Clayton, VIC 3800, Australia

5. School of Computer Software, Tianjin University, Tianjin, China

Publisher

Oxford University Press (OUP)

Subject

Computational Mathematics,Computational Theory and Mathematics,Computer Science Applications,Molecular Biology,Biochemistry,Statistics and Probability

Link

http://academic.oup.com/bioinformatics/advance-article-pdf/doi/10.1093/bioinformatics/bty451/25106235/bty451.pdf

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3. iACP: a sequence-based tool for identifying anticancer peptides;Chen;Oncotarget,2016

4. From antimicrobial to anticancer peptides;Diana;A review. Front. Microbiol.,2013

5. Minimum redundancy feature selection from microarray gene expression data;Ding,2003

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