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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
  1. The fitting parameters from the sigmoid model of regulatory functions of the genes from Table 3.