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Table 2 Sensitivity and specificity values across all {ML algorithm, Dataset} models

From: Machine learning-based predictions of dietary restriction associations across ageing-related genes

Feature type

Num. of features

Num. of instances

Sensitivity

Specificity

Mean

BRF

EEC

XGB

CAT

BRF

EEC

XGB

CAT

Sens

Spec

PathDIP

1640

986 genes

0.87

0.77

0.58

0.77

0.67

0.7

0.93

0.77

0.75

0.77

KEGG-Pertinence

312

799 genes

0.74

0.70

0.75

0.72

0.7

0.71

0.76

0.73

0.73

0.73

KEGG-Influence

1770

799 genes

0.75

0.70

0.67

0.67

0.66

0.71

0.69

0.68

0.70

0.69

PPI-measures

18

850 genes

0.64

0.62

0.6

0.57

0.65

0.57

0.71

0.77

0.61

0.68

PPI-adjacency

5718

850 genes

0.65

0.52

0.5

0.65

0.62

0.74

0.78

0.67

0.58

0.70

GO terms

8640

1124 genes

0.85

0.80

0.48

0.69

0.67

0.72

0.94

0.78

0.71

0.78

GTEx

55

1111 genes

0.54

0.55

0.45

0.57

0.48

0.51

0.57

0.48

0.53

0.51

Co-expression

44,946

1048 genes

0.58

0.61

0.21

0.56

0.45

0.86

0.85

0.54

0.49

0.68

Proteins-Descriptors

156

6180

Proteins

(from

1109 genes)

0.25

0.43

0.45

0.48

0.86

0.91

0.88

0.77

0.40

0.86

Whole-Dataset Imputation

63,099

1137 genes

0.69

0.68

0.43

0.71

0.65

0.63

0.87

0.64

0.63

0.70

Whole-Dataset

Intersection

63,099

628 genes

0.60

0.72

0.39

0.75

0.63

0.62

0.87

0.56

0.62

0.67

Mean

0.65

0.65

0.50

0.65

0.64

0.70

0.80

0.67

0.61

0.70

  1. Sensitivity (\(Ageing_{DR}\) prediction quality) and specificity (\(Ageing_{NotDR}\) prediction quality) values for the BRF, EEC, XGB, and CAT algorithms across all the datasets (feature types). The average sensitivity and specificity of each algorithm across all the datasets is shown in the last row; and the average Gmean and AUC of each feature type across all four algorithms is shown in the last two columns