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Table 1 Prediction accuracy of ACRF and BRNN on the four datasets α,β,α/β,α+β

From: Improving prediction of burial state of residues by exploiting correlation among residues

Methods α β α/β α+β
LR 0.821 ±0.005 0.801 ±0.004 0.808 ±0.003 0.809 ±0.005
BRNN 0.825 ±0.004 0.805 ±0.003 0.812 ±0.004 0.812 ±0.006
ACRF 0.833 ±0.006 0.813 ±0.005 0.818 ±0.003 0.822 ±0.005
ACRF-CN 0.806 ±0.006 0.785 ±0.004 0.787 ±0.003 0.794 ±0.006
ACRF-CN-SC 0.805 ±0.004 0.782 ±0.005 0.783 ±0.005 0.789 ±0.007
ACRF-CN-SC-SS 0.801 ±0.004 0.769 ±0.004 0.773 ±0.005 0.784 ±0.005
  1. For the sake of fair comparison, ACRF and BRNN use identical feature sets. To investigate the effects of different features on prediction accuracy, we evaluated a set of variants of ACRF, including ACRF-CN with contact number removed, ACRF-CN-SC with both contact number and sequence conservation removed, and ACRF-CN-SC-SS with contact number, sequence conservation, and secondary structure information removed from the ACRF model