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. 2020 Dec 18;20(1):e13280. doi: 10.1111/acel.13280

TABLE 2.

The top 10 predictive genes are shown for the top 1000 variable and top 1000 differential aging clocks presented in Figure 3. For each gene, its quantified importance in the random forest prediction model is provided

Aging clock Gene name Protein name Importance
1000 Variable KRT6C Keratin, type II cytoskeletal 6C 14.78989842
1000 Variable KLK6 Kallikrein‐6 14.13181626
1000 Variable SPRR1B Cornifin‐B 12.23451207
1000 Variable MMP10 Stromelysin‐2 10.47366716
1000 Variable CALML3 Calmodulin‐like protein 3 9.350379696
1000 Variable CEACAM5 Carcinoembryonic antigen‐related cell adhesion molecule 5 8.906136426
1000 Variable GRP Gastrin‐releasing peptide 8.344751464
1000 Variable FGA Fibrinogen alpha chain 7.586499443
1000 Variable KRT6B Keratin, type II cytoskeletal 6B 7.481560771
1000 Variable PRSS2 Trypsin‐2 7.316476098
1000 Differential REG3A Regenerating islet‐derived protein 3‐alpha 40.34504236
1000 Differential NR2E1 Nuclear receptor subfamily 2 group E member 1 13.02751594
1000 Differential DEFB119 Beta‐defensin 119 12.62814497
1000 Differential MIR10B N/A 11.9933185
1000 Differential MIR100 N/A 10.6716294
1000 Differential KRT6C Keratin, type II cytoskeletal 6C 10.30831422
1000 Differential DPT Dermatopontin 10.11309756
1000 Differential HLA‐F HLA class I histocompatibility antigen, alpha chain F 9.340543895
1000 Differential KCNC2 Potassium voltage‐gated channel subfamily C member 2 8.923200977
1000 Differential INS Insulin 8.647671153