Haohao Zhou, Hao Wang, Yijie Ding* and Jijun Tang* Pages 1 - 12 ( 12 )
Background: Antifungal Peptides (AFP) have been found to be effective against many fungal infections.
Objective: However, it is difficult to identify AFP. Therefore, it is great practical significance to identify AFP via machine learning methods (with sequence information).
Methods: In this study, a Multi-Kernel Support Vector Machine (MKSVM) with Hilbert-Schmidt Independence Criterion (HSIC) is proposed. Proteins are encoded with five types of features (188-bit, AAC, ASDC, CKSAAP, DPC), and then construct kernels using Gaussian kernel function. HSIC are used to combine kernels and multi-kernel SVM model is built.
Results: Our model performed well on three AFPs datasets and the performance is better than or comparable to other state-of-art predictive models.
Conclusion: Our method will be a useful tool for identifying antifungal peptides.
Antifungal peptides, feature representation, amino acid composition, multiple kernel learning, hilbert-schmidt independence criterion, support vector machine.
School of Computer Science and Technology, College of Intelligence and Computing, Tianjin University, 300354, Tianjin, School of Computer Science and Technology, College of Intelligence and Computing, Tianjin University, 300354, Tianjin, School of Electronic and Information Engineering, Suzhou University of Science and Technology, 215009, Suzhou, Department of Computer Science and Engineering, University of South Carolina, Columbia, SC 29208