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• W2890258727 abstract "Abstract Anti-angiogenic peptides perform distinct physiological functions and potential therapies for angiogenesis-related diseases. Accurate identification of anti-angiogenic peptides may provide significant clues to understand the essential angiogenic homeostasis within tissues and develop antineoplastic therapies. In this study, an ensemble predictor is proposed for anti-angiogenic peptide prediction by fusing an individual classifier with the best sensitivity and another individual one with the best specificity. We investigate predictive capabilities of various feature spaces with respect to the corresponding optimal individual classifiers and ensemble classifiers. The accuracy and Matthew’s Correlation Coefficient (MCC) of the ensemble classifier trained by Bi-profile Bayes (BpB) features are 0.822 and 0.649, respectively, which represents the highest prediction results among the investigated prediction models. Discriminative features are obtained from BpB using the Relief algorithm followed by the Incremental Feature Selection (IFS) method. The sensitivity, specificity, accuracy, and MCC of the ensemble classifier trained by the discriminative features reach up to 0.776, 0.888, 0.832, and 0.668, respectively. Experimental results indicate that the proposed method is far superior to the previous study for anti-angiogenic peptide prediction." @default.
• W2890258727 created "2018-09-27" @default.
• W2890258727 creator A5013785417 @default.
• W2890258727 creator A5084281207 @default.
• W2890258727 date "2018-09-14" @default.
• W2890258727 modified "2023-10-11" @default.
• W2890258727 title "Using a Classifier Fusion Strategy to Identify Anti-angiogenic Peptides" @default.
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• W2890258727 doi "https://doi.org/10.1038/s41598-018-32443-w" @default.
• W2890258727 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/6138733" @default.
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• W2890258727 hasPublicationYear "2018" @default.
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