View : 7 Download: 0

MLACP: machine-learning-based prediction of anticancer peptides

Title
MLACP: machine-learning-based prediction of anticancer peptides
Authors
Manavalan, BalachandranBasith, ShaherinShin, Tae HwanChoi, SunKim, Myeong OkLee, Gwang
Ewha Authors
최선
SCOPUS Author ID
최선scopus
Issue Date
2017
Journal Title
ONCOTARGET
ISSN
1949-2553JCR Link
Citation
vol. 8, no. 44, pp. 77121 - 77136
Keywords
anticancer peptideshybrid modelmachine-learning parametersrandom forestsupport vector machine
Publisher
IMPACT JOURNALS LLC
Indexed
SCIE; SCOPUS WOS
Abstract
Cancer is the second leading cause of death globally, and use of therapeutic peptides to target and kill cancer cells has received considerable attention in recent years. Identification of anticancer peptides (ACPs) through wet-lab experimentation is expensive and often time consuming; therefore, development of an efficient computational method is essential to identify potential ACP candidates prior to in vitro experimentation. In this study, we developed support vector machine- and random forest-based machine-learning methods for the prediction of ACPs using the features calculated from the amino acid sequence, including amino acid composition, dipeptide composition, atomic composition, and physicochemical properties. We trained our methods using the Tyagi-B dataset and determined the machine parameters by 10-fold cross-validation. Furthermore, we evaluated the performance of our methods on two benchmarking datasets, with our results showing that the random forest-based method outperformed the existing methods with an average accuracy and Matthews correlation coefficient value of 88.7% and 0.78, respectively. To assist the scientific community, we also developed a publicly accessible web server at www.thegleelab.org/MLACP.html.
DOI
10.18632/oncotarget.20365
Appears in Collections:
약학대학 > 약학과 > Journal papers
Files in This Item:
There are no files associated with this item.


qrcode

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

BROWSE