Table 4 Prediction results from the SDE sigmoid model corresponding to genes from Table 3
From: A stochastic differential equation model for transcriptional regulatory networks
Target | logL | AIC | QE | Best Fit |
|---|---|---|---|---|
YMR096W(SNZ1) | 7.27 | -8.54 | 6.16 | YMR096W = -0.159 + 0.179 GCN4 + 0.174 HAA1 |
YNR025C(NA) | 3.8 | -1.61 | 5.42 | YNR025C = 0.008 + -0.261 HMS1 + 0.278 ACA1 |
YPR200C(ARR2) | 3.97 | -3.94 | 5.28 | YPR200C = 0.144 + -0.315 INO4 |
YGR234W(YHB1) | 11.08 | -18.17 | 5.28 | YGR234W = 0.059 + -0.12 ARG81 |
YGR269W(NA) | 2.4 | -0.81 | 5.19 | YGR269W = 0.097 + -0.194 HMS1 |
YGL150C(INO80) | 4.25 | -4.51 | 4.41 | YGL150C = -0.082 + 0.168 GAT3 |
YDR193W(NA) | 6.22 | -0.44 | 4.45 | YDR193W = -0.278 + 0.415 LEU3 + 0.166 GAL4 + -0.691 FAP7 + 0.293 CUP9 + 0.375 DAT1 |
YAL061W(NA) | 8.07 | -12.13 | 1.66 | YAL061W = -0.087 + 0.191 CUP9 |
YKL150W(MCR1) | 7.93 | -9.86 | 3.84 | YKL150W = 0.249 + -0.325 CBF1 + -0.175 HAA1 |
YDR515W(SLF1) | 5.94 | 0.12 | 2 | YDR515W = 0.246 + -0.562 CIN5 + -0.347 CBF1 + 0.256 HIR1 + 0.453 HAP4 + -0.304 IFH1 |