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Table 3 Average AUC and number of logical rules according to chi-square test for Sfull and S30 databases.

From: A discriminative method for family-based protein remote homology detection that combines inductive logic programming and propositional models

Methods

S full

AUC (#logical rules)

S30

AUC (#logical rules)

 

no chi-square

δ = 0.05

δ = 0.25

no chi-square

δ = 0.5

δ = 0.25

ILP-SVM-Seq

0.79 (228.59)

0.79 (89.15)

0.75 (59.30)

0.77 (311.09)

0.77 (91.04)

0.70 (26.4)

ILP-SVM-Aln cons

0.81 (44.91)

0.81 (34.98)

0.77 (12.76)

0.81 (56.72)

0.80 (36.44)

0.72 (13.16)

ILP-SVM-Aln ps

0.80 (191.65)

0.79 (139.61)

0.75 (66.15)

0.81 (241.96)

0.81 (178.72)

0.73 (71)

ILP-SVM-Seq-Aln cons

0.85 (311.09)

0.83 (144.07)

0.79 (35.8)

0.80 (381.12)

0.80 (178.56)

0.74 (49.96)

ILP-SVM-Aln cons -Aln ps

0.82 (236.56)

0.82 (174.59)

0.79 (46.04)

0.82 (283.12)

0.82 (209.76)

0.80 (57.28)

ILP-SVM-Seq-Aln cons -Aln ps

0.87 (502.74)

0.85 (283.69)

0.81 (74.3)

0.82 (623.56)

0.82 (357.28)

0.79 (90.96)

ILP-DT-Seq

0.67 (228.59)

0.67 (89.15)

0.62 (59.30)

0.65 (311.09)

0.65 (91.04)

0.61 (26.4)

ILP-DT-Aln cons

0.70 (44.91)

0.70 (34.98)

0.72 (12.76)

0.69 (56.72)

0.69 (36.44)

0.65 (13.16)

ILP-DT-Aln ps

0.68 (191.65)

0.68 (139.61)

0.64 (66.15)

0.67 (241.96)

0.67 (178.72)

0.62 (71)

ILP-DT-Seq-Aln cons

0.72 (311.09)

0.71 (144.07)

0.67 (35.8)

0.69 (381.12)

0.68 (178.56)

0.63 (49.96)

ILP-DT-Aln cons -Aln ps

0.71 (236.56)

0.71 (174.59)

0.73 (46.04)

0.71 (283.12)

0.70 (209.76)

0.62 (57.28)

ILP-DT-Seq-Aln cons -Aln ps

0.74 (502.74)

0.74 (283.69)

0.69 (74.3)

0.71 (623.56)

0.71 (357.28)

0.63 (90.96)