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Current Bioinformatics

Author(s): Haohao Zhou, Hao Wang, Yijie Ding* and Jijun Tang*

DOI: 10.2174/1574893616666210727161003

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Multivariate Information Fusion for Identifying Antifungal Peptides with Hilbert-Schmidt Independence Criterion

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Abstract

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.

Keywords: Antifungal peptides, feature representation, amino acid composition, multiple kernel learning, hilbert-schmidt independence criterion, support vector machine.

Graphical Abstract

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Available from. https://github.com/guofei-tju/Identify-AFP-with-HSIC