Skip to main content

Table 9 Overall performance of various feature classifiers on an 'independent test' dataset for the classification of diverse plastid-types* (phase-I I)

From: Identification and characterization of plastid-type proteins from sequence-attributed features using machine learning

Feature type Sensitivity
(%)
Specificity
(%)
Accuracy
(%)
MCC Precision (%) ER
(%)
AAC 57.26 63.89 72.54 0.30 62.45 27.47
PseAA 57.26 63.88 72.48 0.31 65.25 27.52
Dipep 61.29 65.96 74.97 0.40 73.97 25.03
NCC 61.29 75.82 77.15 0.40 60.42 22.85
Physico-Chem 45.97 65.30 66.63 0.14 47.03 33.37
  1. *classification of 4 plastid types: chloroplast, chromoplast, etioplast, amyloplast; individual performance of these classifiers on each class can be seen in supplementary material; AAC = amino acid composition, PseAA = Pseudo amino acid composition, Dipep = Dipeptide composition, NCC = Nterminal-Center-Cterminal composition (sequence divided into 3 parts), Physico-Chem = Protein physicochemical properties, MCC = Matthews Correlation Coefficient, ER = Error Rate.