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Thus, the SVM classifier was performed for binomial classification of antifungal peptides. The performance of the classifier was evaluated via ten-fold cross-validation and an independent dataset. For further validation of the model developed, 22 P24-derived peptides were predicted using the classifier and in vitro assays were performed on the three peptides with the highest prediction score. The results showed that the PseAAC [Formula: see text] SVM method is able to predict AFPs with ACC of 94.76%. In vitro results also validate the SEN and SPC of the classifier. The results suggest that the computational approach used in this study is highly efficient for prediction of antifungal peptides, which can save time and money in AFP screening and synthesis of novel peptides. <\/jats:p>","DOI":"10.1142\/s0219720018500166","type":"journal-article","created":{"date-parts":[[2018,5,30]],"date-time":"2018-05-30T02:05:05Z","timestamp":1527645905000},"page":"1850016","source":"Crossref","is-referenced-by-count":37,"title":["Computational prediction of antifungal peptides via Chou\u2019s PseAAC and SVM"],"prefix":"10.1142","volume":"16","author":[{"given":"Maryam","family":"Mousavizadegan","sequence":"first","affiliation":[{"name":"Department of Biotechnology, Faculty of Advanced Sciences and Technologies, University of Isfahan, Isfahan, Iran"}]},{"given":"Hassan","family":"Mohabatkar","sequence":"additional","affiliation":[{"name":"Department of Biotechnology, Faculty of Advanced Sciences and Technologies, University of Isfahan, Isfahan, 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