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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