A practical approach to feature selection
ML92 Proceedings of the ninth international workshop on Machine learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
Prediction of Protein Secondary Structure with two-stage multi-class SVMs
International Journal of Data Mining and Bioinformatics
An asymmetric classifier based on partial least squares
Pattern Recognition
Novel maximum-margin training algorithms for supervised neural networks
IEEE Transactions on Neural Networks
Prediction of alternatively spliced exons using Support Vector Machines
International Journal of Data Mining and Bioinformatics
International Journal of Data Mining and Bioinformatics
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Mathematical and Computer Modelling: An International Journal
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Some strains of avian influenza A virus AIV can directly transmit from their natural hosts to humans. These avian-to-human transmissions have continuously been reported to cause human deaths worldwide since 1997. Predicting whether AIV strains can transmit from avian to human is valuable for early warning of AIV strains with human pandemic potential. In this study, we constructed a computational model to predict avian-to-human transmission of AIV based on physicochemical properties. Initially, ninety signature positions in the inner protein sequences were extracted with the entropy method. These positions were then encoded with 531 physicochemical features. Subsequently, the optimal subset of these physicochemical features was mined with several feature selection methods. Finally, a support vector machine SVM model named A2H was established to integrate the selected optimal features. The experimental results of cross-validation and an independent test show that A2H has the capability of predicting transmission of AIV from avian to human.