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Table 1.

Breakdown of the benchmark dataset (cf. Eq. 1) used in this study.

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Table 2.

List of the values of the ten physical-chemical properties for each of the 20 native amino acids.

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Figure 1.

An illustration to show two types of covariance.

(a) The auto-covariance refers to the coupling between two subsequences from a same sequence when they are separated by unit. (b) The cross-covariance refers to the coupling between two subsequences from two different sequences as indicated by two open curly braces.

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Figure 2.

A flowchart to show the prediction process of iNR-PhysChem.

T1 represents the benchmark dataset from [16] for training the 1st-level prediction; T2 represents the benchmark dataset from [16] for training the 2nd-level prediction. See the text for further explanation.

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Figure 3.

An illustration to show the predicted results fallen into four different quadrants.

(I) TP, the true positive quadrant (green) for correct prediction of positive dataset, (II) FP, the false positive quadrant (red) for incorrect prediction of negative dataset; (III) TN, the true negative quadrant (blue) for correct prediction of negative dataset; and (IV) FN, the false negative quadrant (pink) for incorrect prediction of positive dataset.

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Figure 4.

A semi-screenshot to see the top page of iNR-PhysChem.

The web-server is at either http://www.jci-bioinfo.cn/iNR-PhysChem or http://icpr.jci.edu.cn/bioinfo/iNR-PhysChem.

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Figure 5.

The 3D graph to show the success rates by the 5-fold cross-validation with different values of C and in the SVM engine.

(a) The results obtained for the 1st-level prediction. (b) The results obtained for the 2nd-level prediction.

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Table 3.

Comparison of the success rates and MCC values obtained by the current iNR-PhysChem and NR-2L [16] in identifying NRs and non-NRs by the jackknife test on the benchmark dataset (cf. Eq. 1).

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Table 4.

Comparison of the success rates and MCC values obtained by the current iNR-PhysChem and NR-2L [16] in identifying the subfamilies of NRs by the jackknife test on the benchmark dataset (cf. Eq. 1).

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