Table 1 

Definition of the seven scoring functions used in PIA v. 2.0. 

Metric 
Description 
Formula^{a} 


1 
%Correct 
(NTP + NTN)/(NTP + NFN + NFP + NTN) 
2 
Sensitivity + Specificity 
[NTP/(NTP+NFN)] + [NTN/(NFP+NTN)] 
3 
Positive Predictive Value (PPV)+ Negative Predictive Value (NPV) 
[NTP/(NTP+NFP)] + [NTN/(NFN+NTN)] 
4 
Risk Ratio 
[(NTP)(NFP+NTN)]/[(NFP)(NTP+NFN)] 
5 
Odds Ratio 
[(NTP)(NTN)]/[(NFP)(NFN)] 
6 
Gini Index^{b} 
GINI_{parent } GINI_{split} 
GINI(k) = 1.0  ∑_{j = 1, J }[p(jk)] 

GINI_{split }= ∑_{k = 1, K }[(n_{k}/n) GINI(k)] 

7 
Absolute Probability Difference^{c} 
Σ_{k = 1, K }P_{1}(k)  P_{2}(k) 


^{a }NTP, Number of true positives; NTN, number of true negatives; NFN, number of false negatives; NFP, number of false positives. ^{b }Gini Index is used in CART decision trees [25]. The scoring for Gini index is described under "Algorithm." ^{c }Scoring is the probability of finding a case (P_{1}) in cell, k, minus the probability of observing a control (P_{2}) in cell, k, summed over all the K cells in the genotypephenotype table. 

Mechanic et al. BMC Bioinformatics 2008 9:146 doi:10.1186/147121059146 