Antifungal peptides (AFP) have been found to be effective against many fungal infections.
However, it is difficult to identify AFP. Therefore, it is great practical significance to identify AFP via machine learning methods (with sequence information).
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.
Our model performed well on three AFPs datasets and the performance is better than or comparable to other state-of-art predictive models.
Our method will be a useful tool for identifying antifungal peptides.