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dc.contributor.authorThet Su Winen_US
dc.contributor.authorNalini Schaduangraten_US
dc.contributor.authorVirapong Prachayasittikulen_US
dc.contributor.authorChanin Nantasenamaten_US
dc.contributor.authorWatshara Shoombuatongen_US
dc.contributor.otherMahidol Universityen_US
dc.contributor.otherUniversity of Medical Technologyen_US
dc.identifier.citationFuture Medicinal Chemistry. Vol.10, No.15 (2018), 1749-1767en_US
dc.description.abstract© 2018 Newlands Press. Aim: Hypertension is associated with development of cardiovascular disease and has become a significant health problem worldwide. Naturally-derived antihypertensive peptides have emerged as promising alternatives to synthetic drugs. Materials & methods: This study introduces predictor of antihypertensive activity of peptides constructed using random forest classifier as a function of various combinations of amino acid, dipeptide and pseudoamino acid composition descriptors. Results: Classification models were assessed via independent test set that demonstrated accuracy of 84.73%. Feature importance analysis revealed the preference of proline and hydrophobic amino acids at the C-terminal as well as the preference of short peptides for robust activity. Conclusion: Model presented herein serves as a useful tool for predicting and analysis of antihypertensive activity of peptides.en_US
dc.rightsMahidol Universityen_US
dc.subjectBiochemistry, Genetics and Molecular Biologyen_US
dc.subjectPharmacology, Toxicology and Pharmaceuticsen_US
dc.titlePAAP: A web server for predicting antihypertensive activity of peptidesen_US
Appears in Collections:Scopus 2018

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