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Prediction of cell-penetrating peptides with feature selection techniques.

Abstract：

:Cell-penetrating peptides are a group of peptides which can transport different types of cargo molecules such as drugs across plasma membrane and have been applied in the treatment of various diseases. Thus, the accurate prediction of cell-penetrating peptides with bioinformatics methods will accelerate the development of drug delivery systems. The study aims to develop a powerful model to accurately identify cell-penetrating peptides. At first, the peptides were translated into a set of vectors with the same dimension by using dipeptide compositions. Secondly, the Analysis of Variance-based technique was used to reduce the dimension of the vector and explore the optimized features. Finally, the support vector machine was utilized to discriminate cell-penetrating peptides from non-cell-penetrating peptides. The five-fold cross-validated results showed that our proposed method could achieve an overall prediction accuracy of 83.6%. Based on the proposed model, we constructed a free webserver called C2Pred (http://lin.uestc.edu.cn/server/C2Pred).

journal_name

Biochem Biophys Res Commun

journal_title

Biochemical and biophysical research communications

authors

Tang H,Su ZD,Wei HH,Chen W,Lin H

doi

10.1016/j.bbrc.2016.06.035

subject

Has Abstract

pub_date

2016-08-12 00:00:00

pages

150-154

issue

1

eissn

0006-291X

issn

1090-2104

pii

S0006-291X(16)30953-6

journal_volume

477

pub_type

杂志文章

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