CPPred-FL: a sequence-based predictor for large-scale identification of cell-penetrating peptides by feature representation learning-Reference-Cited by-同舟云学术

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CPPred-FL: a sequence-based predictor for large-scale identification of cell-penetrating peptides by feature representation learning

Published:2018-09-17 Issue: Volume: Page:
ISSN:1467-5463
Container-title:Briefings in Bioinformatics
language:en
Short-container-title:

Author:

Qiang Xiaoli¹,Zhou Chen²,Ye Xiucai³,Du Pu-feng⁴,Su Ran⁴,Wei Leyi²^ORCID

Affiliation:

1. Institute of Computing Science and Technology, Guangzhou University, Guangzhou, China

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

3. Department of Computer Science, University of Tsukuba, Tsukuba, Japan

4. School of Software, Tianjin University, Tianjin, China

Funder

Natural Science Foundation of China

Publisher

Oxford University Press (OUP)

Subject

Molecular Biology,Information Systems

Link

http://academic.oup.com/bib/advance-article-pdf/doi/10.1093/bib/bby091/25740396/bby091.pdf

Reference57 articles.

1. Mechanisms of cellular uptake of cell-penetrating peptides;Madani;J Biophys,2011

2. Cell-penetrating peptides: classes, origin, and current landscape;Milletti;Drug Discov Today,2012

3. Cell-penetrating peptides: from basic research to clinics;Guidotti;Trends Pharmacol Sci,2017

4. Cellular uptake of the tat protein from human immunodeficiency virus;Frankel;Cell,1988

5. Scrape-loading and dye transfer: a rapid and simple technique to study gap junctional intercellular communication;El-Fouly;Exp Cell Res,1987

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