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1.
Figure 5

Figure 5. From: An improved sequence based prediction protocol for DNA-binding proteins using SVM and comprehensive feature analysis.

The IFS curves of DNAdset, DNArset and DNAaset.

Chuanxin Zou, et al. BMC Bioinformatics. 2013;14:90-90.

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2.
Figure 2

Figure 2. From: An improved sequence based prediction protocol for DNA-binding proteins using SVM and comprehensive feature analysis.

The count of three kinds of Dipeptide composition D0, D1, D2.

Chuanxin Zou, et al. BMC Bioinformatics. 2013;14:90-90.

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3.
Figure 4

Figure 4. From: An improved sequence based prediction protocol for DNA-binding proteins using SVM and comprehensive feature analysis.

The performance of different AC features with various LG values over DNAdset and DNAaset.

Chuanxin Zou, et al. BMC Bioinformatics. 2013;14:90-90.

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4.
Figure 3

Figure 3. From: An improved sequence based prediction protocol for DNA-binding proteins using SVM and comprehensive feature analysis.

Definitions of the N-terminal, middle, and C-terminal parts depending on sequence length L for SAA method.

Chuanxin Zou, et al. BMC Bioinformatics. 2013;14:90-90.

CitationFull text

5.
Figure 6

Figure 6. From: An improved sequence based prediction protocol for DNA-binding proteins using SVM and comprehensive feature analysis.

Distribution of the number of each type of features (a total 12 types) in the optimal feature set.

Chuanxin Zou, et al. BMC Bioinformatics. 2013;14:90-90.

CitationFull text

6.
Figure 1

Figure 1. From: An improved sequence based prediction protocol for DNA-binding proteins using SVM and comprehensive feature analysis.

The overall workflow of the present method. Firstly, the input amino acid sequence is represented numerically by four kinds of features. Secondly, these feature values are transformed to feature descriptor matrices from three different levels. Thirdly, the first round of the evaluation is adopted based on the original descriptor pool and individual SVM models obtained. At last, mRMR-IFS feature selection method and ensemble learning approach are applied as the final evaluation of the optimal SVM model.

Chuanxin Zou, et al. BMC Bioinformatics. 2013;14:90-90.

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