PaPI: pseudo amino acid composition to score human protein-coding variants

BMC Bioinformatics

Table 3 Performances of RF and LR on the three test sets

Test Set	Tool	AUC	Accuracy [IC95%]	Sens	Spec	PPV	NPV	F-m	MCC
# 1	RF	.8988	.8314 [.8381-.8246]	.8354	.8274	.8298	.8331	.8326	.6629
# 1	LR	.8770	.8118 [.8188-.8047]	.8410	.7825	.7957	.8301	.8177	.6246
# 2	RF	.90	.8310 [.8377-.8242]	.8370	.8250	.8282	.8340	.8325	.6621
# 2	LR	.8752	.8121 [.8190-8049]	.8464	.7775	.7931	.8340	.8189	.6255
# 3	RF	.9035	.8344 [.8422-8262]	.8406	.8280	.8311	.8377	.8358	.6687
# 3	LR	.8833	.8168 [.8250-.8083]	.8459	.7875	.8003	.8355	.8225	.6346

Performances of the Random Forest (RF) and Logistic Regression (LR) on the three test sets. Area under the curve (AUC), accuracy with 95% confidence interval, sensitivity (Sens), specificity (Spec), Positive Predictive Value (PPV), Negative Predictive Value (NPV), F-measure (F-m) and Matthews correlation coefficient (MCC) are reported for each method.

ISSN: 1471-2105