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Please use this identifier to cite or link to this item: http://repository.li.mahidol.ac.th/dspace/handle/123456789/50158
Title: THPep: A machine learning-based approach for predicting tumor homing peptides
Authors: Watshara Shoombuatong
Nalini Schaduangrat
Reny Pratiwi
Chanin Nantasenamat
Setia Budi University
Mahidol University
Keywords: Biochemistry, Genetics and Molecular Biology;Chemistry;Mathematics
Issue Date: 1-Jun-2019
Citation: Computational Biology and Chemistry. Vol.80, (2019), 441-451
Abstract: © 2019 Elsevier Ltd In the present era, a major drawback of current anti-cancer drugs is the lack of satisfactory specificity towards tumor cells. Despite the presence of several therapies against cancer, tumor homing peptides are gaining importance as therapeutic agents. In this regard, the huge number of therapeutic peptides generated in recent years, demands the need to develop an effective and interpretable computational model for rapidly, effectively and automatically predicting tumor homing peptides. Therefore, a sequence-based approach referred herein as THPep has been developed to predict and analyze tumor homing peptides by using an interpretable random forest classifier in concomitant with amino acid composition, dipeptide composition and pseudo amino acid composition. An overall accuracy and Matthews correlation coefficient of 90.13% and 0.76, respectively, were achieved from the independent test set on an objective benchmark dataset. Upon comparison, it was found that THPep was superior to the existing method and holds high potential as a useful tool for predicting tumor homing peptides. For the convenience of experimental scientists, a web server for this proposed method is provided publicly at http://codes.bio/thpep/.
URI: http://repository.li.mahidol.ac.th/dspace/handle/123456789/50158
metadata.dc.identifier.url: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85066295458&origin=inward
ISSN: 14769271
Appears in Collections:Scopus 2019

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