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Title: Prediction of therapeutic peptides by incorporating q-Wiener index into Chou's general PseAAC
Authors: Xu C.
Ge L.
Zhang, Yusen
Dehmer M.
Gutman, Ivan
Issue Date: 2017
Abstract: © 2017 Elsevier Inc. As therapeutic peptides have been taken into consideration in disease therapy in recent years, many biologists spent time and labor to verify various functional peptides from a large number of peptide sequences. In order to reduce the workload and increase the efficiency of identification of functional proteins, we propose a sequence-based model, q-FP (functional peptide prediction based on the q-Wiener Index), capable of recognizing potentially functional proteins. We extract three types of features by mixing graphic representation and statistical indices based on the q-Wiener index and physicochemical properties of amino acids. Our support-vector-machine-based model achieves an accuracy of 96.71%, 93.34%, 98.40%, and 91.40% for anticancer, virulent, and allergenic proteins datasets, respectively, by using 5-fold cross validation.
Type: article
DOI: 10.1016/j.jbi.2017.09.011
ISSN: 1532-0464
SCOPUS: 2-s2.0-85030483326
Appears in Collections:Faculty of Medical Sciences, Kragujevac

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