From: Predicting protein residue-residue contacts using random forests and deep networks
Method | Top 10 | L/10 | L/5 |
---|---|---|---|
CONSIP2 (94) | 91.29% | 87.23% | 83.29% |
RaptorX-Contact (94) | 89.89% | 87.02% | 83.17% |
MULTICOM-NOVEL (94) | 85.93% | 84.16% | 82.73% |
rf_full (78) | 85.13% | 82.07% | 77.37% |
rf_select (78) | 80.38% | 77.63% | 73.83% |
PLCT (91) | 75.70% | 76.46% | 76.50% |
sda_Ensemble (78) | 75.51% | 72.53% | 71.39% |
svm (78) | 73.08% | 72.61% | 72.57% |
sda_balanced (78) | 71.79% | 68.89% | 66.28% |
sda_unbalanced (78) | 71.77% | 71.50% | 71.50% |
IASL-COPE (91) | 54.67% | 53.23% | 53.22% |
Pcons-net (87) | 49.64% | 47.98% | 47.76% |
raghavagps-paaint (76) | 46.45% | 47.18% | 47.21% |
DCA_cpp(88) | 38.54% | 36.71% | 34.57% |
FoDTcm (32) | 31.25% | 34.01% | 34.95% |