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PFP RFSM: Protein fold prediction by using random forests and sequence motifs

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Figure

Figure 1. Architecture of random forest classifier. Random forest generally includes 4 steps
Table 1.  Success rates of random forest and other 5 machine learning classifiers. The best results for each fold are shown in bold
Table 2.  Matthews’s correlation coefficients (MCC) calculated for random forest and other 5 machine learning classifiers
Table 3.  Comparison between PFP-RFSM and 10 representative protein fold predictors on success rates
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