4.1. Criteria for Evaluation of Performance Measures
In this paper, we use sensitivity, specificity, precision, and accuracy as performance measures for peptide classification [26]. We interpret the word “positive” as “differentially phosphorylated” and the term “negative” as “non-differentially phosphorylated”. In addition, we use the following notations: ∥∥ operator denotes the size of a set.
TP (True Positives): the set of all differentially phosphorylated peptides predicted as differentially phosphorylated.
FN (False Negatives): the set of all differentially phosphorylated peptides predicted as non-differentially phosphorylated.
TN (True Negatives): the set of all non-differentially phosphorylated peptides predicted as non-differentially phosphorylated.
FP (False Positives): the set of all non-differentially phosphorylated peptides predicted as differentially phosphorylated.
The specificity criterion shows the proportion of all true negatives classified correctly, and is defined as follows: (2) Specificity=∥TN∥∥TN∥+∥FP∥
The sensitivity score, which is also referred to as recall, shows the proportion of all positives classified correctly, and is defined as follows: (3) Sensitivity=∥TP∥∥TP∥+∥FN∥
The precision criterion shows the proportion of all true positive samples against all the positive results, and is defined as follows: (4) Precision=∥TP∥∥TP∥+∥FP∥
Accuracy is the proportion of all samples classified correctly, and is defined as follows: (5) Accuracy=∥TP∥+∥TN∥∥TP∥+∥TN∥+∥FP∥+∥FN∥