MK-FSVM-SVDD: A Multiple Kernel-based Fuzzy SVM Model for Predicting DNA-binding Proteins via Support Vector Data Description

(E-pub Ahead of Print)

Author(s): Yi Zou, Hongjie Wu, Xiaoyi Guo, Li Peng, Yijie Ding*, Jijun Tang, Fei Guo

Journal Name: Current Bioinformatics

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Background: Detecting DNA-binding proetins (DBPs) based on biological and chemical methods is time consuming and expensive.

Objective: In recent years, the rise of computational biology methods based on Machine Learning (ML) has greatly improved the detection efficiency of DBPs.

Method: In this study, Multiple Kernel-based Fuzzy SVM Model with Support Vector Data Description (MK-FSVM-SVDD) is proposed to predict DBPs. Firstly, sex features are extracted from protein sequence. Secondly, multiple kernels are constructed via these sequence feature. Than, multiple kernels are integrated by Centered Kernel Alignment-based Multiple Kernel Learning (CKA-MKL). Next, fuzzy membership scores of training samples are calculated with Support Vector Data Description (SVDD). FSVM is trained and employed to detect new DBPs.

Results: Our model is test on several benchmark datasets. Compared with other methods, MK-FSVM-SVDD achieves best Matthew's Correlation Coefficient (MCC) on PDB186 (0.7250) and PDB2272 (0.5476).

Conclusion: We can conclude that MK-FSVM-SVDD is more suitable than common SVM, as the classifier for DNA-binding proteins identification.

Keywords: DNA-binding proteins, Fuzzy support vector machine, Multiple kernel learning, Support vector data description, Membership function.

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(E-pub Ahead of Print)
DOI: 10.2174/1574893615999200607173829
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