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Table 2 Results for multi-class fold prediction in the remote homology detection set-up. Results for the adaptive codes method are reported for a SCOP benchmark data set (44 folds, 424 superfamilies, 809 families, with 381 test sequences).

From: SVM-Fold: a tool for discriminative multi-class protein fold and superfamily recognition

Method (and optimization target) Error Balanced Error Top 5 Error Balanced Top 5 Error Detection Rate at fdr = 1% Detection Rate at fdr = 5%
PSI-BLAST 0.4094 0.4428 0.2966 0.3666 0.3123 0.3412
one-vs-all: Folds 0.3307 0.4565 0.1260 0.1954 0.2073 0.2493
one-vs-all: Folds, Sfams 0.3307 0.4565 0.1260 0.1954 0.2073 0.2336
Sigmoid Fitting: Folds 0.3465 0.4973 0.1706 0.3407 0.2178 0.2283
Adaptive Codes: Folds (zero-one) 0.3018 0.3769 0.1286 0.1862 0.2073 0.2362
Adaptive Codes: Folds (balanced) 0.3281 0.3766 0.1680 0.1702 0.2073 0.2362
Adaptive Codes: Folds, Sfams (zero-one) 0.2808 0.3749 0.1155 0.1770 0.2283 0.2493
Adaptive Codes: Folds, Sfams (balanced) 0.2887 0.3659 0.1260 0.1427 0.2231 0.2388
Adaptive Codes: Folds, Sfams, Fams (zero-one) 0.2493 0.3474 0.1024 0.1726 0.2231 0.2677
Adaptive Codes: Folds, Sfams, Fams (balanced) 0.2703 0.3445 0.1155 0.1418 0.2283 0.2703