Table 2.
Age prediction performances of the different statistical models on the training and testing sets.
Model | Best performance from Training (T)/Testing (V) setsa | Number of CpGs | CpG combination | Training set | Testing set | ||||
---|---|---|---|---|---|---|---|---|---|
R | MAD | RMSE | R | MAD | RMSE | ||||
Zbiec-Pierkarska 1 | – | 2 | CpG5,7 | 0.918 | 6.885 | 9.127 | 0.932 | 6.397 | 8.803 |
MQR | T | 9 | CpG1–2 & 4–6 & CpG22,42,62–72 | 0.945 | 5.133 | 6.975 | 0.950 | 4.773 | 6.730 |
V | 8 | CpG4–6 & CpG22–42,62–72 | 0.941 | 5.229 | 7.184 | 0.953 | 4.574 | 6.559 | |
SVMr | T | 6 | CpG1–3,5–7 | 0.956 | 4.555 | 6.229 | 0.953 | 4.464 | 6.544 |
V | 5 | CpG2–3,5–7 | 0.9546 | 4.6139 | 6.3257 | 0.9534 | 4.4101 | 6.4919 | |
SVMl | T | 7 | CpG1–7 | 0.935 | 5.575 | 7.531 | 0.943 | 5.221 | 7.194 |
V | 5 | CpG2–6 | 0.930 | 5.650 | 7.793 | 0.945 | 5.130 | 7.058 | |
SVMp | T | 7 | CpG1–7 | 0.799 | 9.946 | 13.046 | 0.830 | 9.734 | 12.124 |
V | 5 | CpG3–7 | 0.778 | 10.456 | 13.582 | 0.833 | 9.465 | 12.098 | |
GBR | T | 7 | CpG1–7 | 0.992 | 1.993 | 2.627 | 0.953 | 4.549 | 6.520 |
V | 5 | CpG2,4–7 | 0.989 | 2.378 | 3.121 | 0.955 | 4.426 | 6.398 | |
mMDA | T | 3 | CpG1,5–6 | 0.933 | 5.650 | 7.625 | 0.940 | 5.320 | 7.357 |
V | 3 | CpG2,5–6 | 0.929 | 5.801 | 7.855 | 0.943 | 5.231 | 7.223 |
aFor each statistical model, both CpG combinations giving the best age prediction accuracy according to the training (T) and testing (V) sets were included in the table.