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dc.contributor.authorWatshara Shoombuatongen_US
dc.contributor.authorNalini Schaduangraten_US
dc.contributor.authorReny Pratiwien_US
dc.contributor.authorChanin Nantasenamaten_US
dc.contributor.otherSetia Budi Universityen_US
dc.contributor.otherMahidol Universityen_US
dc.identifier.citationComputational Biology and Chemistry. Vol.80, (2019), 441-451en_US
dc.description.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
dc.rightsMahidol Universityen_US
dc.subjectBiochemistry, Genetics and Molecular Biologyen_US
dc.titleTHPep: A machine learning-based approach for predicting tumor homing peptidesen_US
Appears in Collections:Scopus 2019

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