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Title: PepBio: Predicting the bioactivity of host defense peptides
Authors: Saw Simeon
Hao Li
Thet Su Win
Aijaz Ahmad Malik
Abdul Hafeez Kandhro
Theeraphon Piacham
Watshara Shoombuatong
Pornlada Nuchnoi
Jarl E.S. Wikberg
M. Paul Gleeson
Chanin Nantasenamat
Mahidol University
Kasetsart University
Uppsala Universitet
King Mongkut's Institute of Technology Ladkrabang
Keywords: Chemical Engineering;Chemistry
Issue Date: 1-Jan-2017
Citation: RSC Advances. Vol.7, No.56 (2017), 35119-35134
Abstract: © 2017 The Royal Society of Chemistry. Host defense peptides (HDPs) represents a class of ubiquitous and rapid responding immune molecules capable of direct inactivation of a wide range of pathogens. Recent research has shown HDPs to be promising candidates for development as a novel class of broad-spectrum chemotherapeutic agent that is effective against both pathogenic microbes and malignant neoplasm. This study aims to quantitatively explore the relationship between easy-to-interpret amino acid composition descriptors of HDPs with their respective bioactivities. Classification models were constructed using the C4.5 decision tree and random forest classifiers. Good predictive performance was achieved as deduced from the accuracy, sensitivity and specificity in excess of 90% and Matthews correlation coefficient in excess of 0.5 for all three evaluated data subsets (e.g. training, 10-fold cross-validation and external validation sets). The source code and data set used for the construction of classification models are available on GitHub at
ISSN: 20462069
Appears in Collections:Scopus 2016-2017

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