From: Modelling biochemical networks with intrinsic time delays: a hybrid semi-parametric approach
NN | τ i | BIC | MSE | NN | τ i | BIC | MSE | ||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
train | valid | test | train | valid | test | train | valid | test | train | valid | test | ||||
5 | 0 | -12217 | -5836 | -5997 | 0.0141 | 0.0152 | 0.0210 | 6 | 0 | -12220 | -5869 | -6039 | 0.0139 | 0.0157 | 0.0222 |
2 | 100 | -13118 | -6209 | -6190 | 0.0368 | 0.0350 | 0.0337 | 2 | 110 | -13058 | -6150 | -6157 | 0.0347 | 0.0310 | 0.0315 |
3 | 100 | -13087 | -6273 | -6336 | 0.0350 | 0.0384 | 0.0437 | 3 | 110 | -13043 | -6269 | -6275 | 0.0334 | 0.0381 | 0.0385 |
4 | 100 | -11826 | -5650 | -5888 | 0.0096 | 0.0105 | 0.0170 | 4 | 110 | -12273 | -5805 | -5832 | 0.0151 | 0.0144 | 0.0152 |
5 | 100 | -11386 | -5379 | -5733 | 0.0060 | 0.0059 | 0.0120 | 5 | 110 | -12302 | -5864 | -6008 | 0.0152 | 0.0156 | 0.0210 |
6 | 100 | -12873 | -6174 | -6176 | 0.0265 | 0.0282 | 0.0284 | 6 | 110 | -13162 | -6336 | -6329 | 0.0355 | 0.0392 | 0.0386 |
7 | 100 | -13144 | -6269 | -6176 | 0.0342 | 0.0330 | 0.0273 | 7 | 110 | -11516 | -5572 | -5731 | 0.0066 | 0.0081 | 0.0111 |
2 | 120 | -13047 | -6148 | -6139 | 0.0343 | 0.0309 | 0.0303 | 2 | 130 | -13242 | -6332 | -6371 | 0.0417 | 0.0449 | 0.0486 |
3 | 120 | -12105 | -5782 | -5960 | 0.0130 | 0.0142 | 0.0204 | 3 | 130 | -13076 | -6173 | -6203 | 0.0346 | 0.0314 | 0.0333 |
4 | 120 | -11974 | -5761 | -5891 | 0.0111 | 0.0132 | 0.0171 | 4 | 130 | -12652 | -6087 | -6090 | 0.0221 | 0.0254 | 0.0256 |
5 | 120 | -11436 | -5462 | -5489 | 0.0062 | 0.0068 | 0.0071 | 5 | 130 | -11823 | -5604 | -5676 | 0.0094 | 0.0092 | 0.0107 |
6 | 120 | -10820 | -5170 | -5714 | 0.0033 | 0.0036 | 0.0108 | 6 | 130 | -12679 | -6093 | -6108 | 0.0218 | 0.0240 | 0.0247 |
7 | 120 | -12533 | -6002 | -5881 | 0.0184 | 0.0193 | 0.0151 | 7 | 130 | -13269 | -6384 | -6393 | 0.0388 | 0.0417 | 0.0424 |
2 | 140 | -13069 | -6155 | -6167 | 0.0351 | 0.0313 | 0.0321 | 2 | 160 | -13195 | -6295 | -6257 | 0.0398 | 0.0416 | 0.0385 |
3 | 140 | -12303 | -5805 | -5803 | 0.0158 | 0.0149 | 0.0149 | 3 | 160 | -12252 | -5823 | -5771 | 0.0151 | 0.0155 | 0.0139 |
4 | 140 | -13288 | -6375 | -6384 | 0.0420 | 0.0455 | 0.0464 | 4 | 160 | -13063 | -6186 | -6241 | 0.0334 | 0.0311 | 0.0347 |
5 | 140 | -12537 | -6043 | -6039 | 0.0193 | 0.0225 | 0.0223 | 5 | 160 | -12022 | -5716 | -5909 | 0.0114 | 0.0116 | 0.0171 |
6 | 140 | -12564 | -6067 | -6078 | 0.0194 | 0.0228 | 0.0233 | 6 | 160 | -12052 | -5800 | -5995 | 0.0116 | 0.0133 | 0.0197 |
7 | 140 | -11439 | -5535 | -5994 | 0.0061 | 0.0075 | 0.0189 | 7 | 160 | -11466 | -5431 | -5441 | 0.0063 | 0.0061 | 0.0062 |
2 | 80, 120 | -13016 | -6146 | -6079 | 0.0330 | 0.0305 | 0.0266 | 2 | 120, 160 | -12984 | -6137 | -6027 | 0.0320 | 0.0299 | 0.0240 |
3 | 80, 120 | -12334 | -5860 | -5968 | 0.0162 | 0.0164 | 0.0204 | 3 | 120, 160 | -13115 | -6296 | -6163 | 0.0357 | 0.0397 | 0.0303 |
4 | 80, 120 | -11221 | -5276 | -5566 | 0.0051 | 0.0048 | 0.0087 | 4 | 120, 160 | -12250 | -5872 | -5934 | 0.0145 | 0.0162 | 0.0183 |
5 | 80, 120 | -12780 | -6221 | -6207 | 0.0243 | 0.0314 | 0.0305 | 5 | 120, 160 | -12293 | -5872 | -5984 | 0.0148 | 0.0155 | 0.0194 |
6 | 80, 120 | -12233 | -5837 | -5944 | 0.0136 | 0.0139 | 0.0172 | 6 | 120, 160 | -11240 | -5352 | -7991 | 0.0050 | 0.0052 | 1.0762 |
7 | 80, 120 | -11688 | -5663 | -5630 | 0.0077 | 0.0094 | 0.0088 | 7 | 120, 160 | -11703 | -5623 | -6004 | 0.0078 | 0.0086 | 0.0187 |
2 | 80, 120,160 | -12994 | -6144 | -6034 | 0.0321 | 0.0300 | 0.0241 | 5 | 80, 120, 160 | -12487 | -5953 | -6045 | 0.0178 | 0.0178 | 0.0215 |
3 | 80, 120, 160 | -11855 | -5641 | -5937 | 0.0099 | 0.0104 | 0.0189 | 6 | 80, 120, 160 | -12824 | -6193 | -6213 | 0.0244 | 0.0276 | 0.0288 |
4 | 80, 120, 160 | -11879 | -5605 | -5734 | 0.0099 | 0.0092 | 0.0120 | 7 | 80, 120, 160 | -12167 | -5758 | -5774 | 0.0122 | 0.0110 | 0.0113 |