First survey and functional annotation of prohormone and convertase genes in the pig

BMC Genomics

Table 4 Performance of various cleavage prediction models to predict cleavage in pig prohormones

Performance	Known	Mammalian	Human	Logistic	Human	ANN^d
Criteria^a	Motif	Logistic	AA^b	AA Prop.^c	AA	AA Prop.
True Positives	181	165	160	158	164	167
True Negatives	1520	1640	1724	1670	1735	1747
False Positives	329	209	125	179	114	102
False Negatives	54	70	75	77	71	68
Correct Classification	0.8162	0.8661	0.904	0.8772	0.9112	0.9184
Sensitivity	0.7702	0.7021	0.6809	0.6723	0.6979	0.7106
Specificity	0.8221	0.887	0.9324	0.9032	0.9383	0.9448
Positive predictive power	0.3549	0.4412	0.5614	0.4688	0.5899	0.6208
Negative predictive power	0.9657	0.9591	0.9583	0.9559	0.9607	0.9625
Correlation	0.4358	0.4856	0.5645	0.4944	0.5919	0.6184
AUC	0.8006	0.847	0.86	0.8186	0.8589	0.8802

^a Performance criteria. True positives: number of correctly predicted cleaved sites; True negatives: number of correctly predicted non-cleaved sites; False positives: number of incorrectly predicted cleaved sites; False negatives: number of incorrectly predicted non-cleaved sites; Correct classification rate: number of correctly predicted sites divided by the total number of sites; Sensitivity (one minus false positive rate): number of true positives divided by the total number of sites cleaved; Specificity (one minus false negative rate): number of true negatives divided by the total number of sites not cleaved; Positive predictive power: number of true positives divided by the total number of sites predicted to be cleaved; Negative predictive power: number of true negatives divided by the total number of sites predicted to not be cleaved; Correlation coefficient: Mathew’s correlation coefficient between observed and predicted cleavage; and AUC: Area under the receiver operator characteristic or ROC curve relating sensitivity and 1-specificity.
^b AA: models trained only on amino acids.
^c AA prop: models trained with amino acids combined with the physicochemical properties of amino acids.
^d ANN: artificial neural network approach.

ISSN: 1471-2164