Skip to main content

Table 3 Prediction results from the SDE beta sigmoid model for selected genes

From: A stochastic differential equation model for transcriptional regulatory networks

Target logL AIC QE Best Fit
YMR096W(SNZ1) 8.86 -11.72 0.8 YMR096W = -0.069 + 0.330 HAP3 + 0.115 CIN5
YNR025C(NA) 13.7 -13.4 0.11 YNR025C = 0.033 + -0.556 ARG81 + 0.487 HSF1+ 0.195 FAP7 + -0.120 FKH1 + -0.319 DAL81 + 0.141 GCR2
YPR200C(ARR2) 13.04 -14.08 0.29 YPR200C = 0.00037 + -0.707 GAL4 + 0.369 INO4 + 0.364 HAP2 + -0.201 ABF1 + 0.129 FAP7
YGR234W(YHB1) 15.27 -24.53 0.46 YGR234W = -0.042 + -0.157 HIR1 + 0.139 ABF1
YGR269W(NA) 12.42 -14.84 0.48 YGR269W = 0.011 + -0.263 GZF3 + 0.313 CRZ1 + -0.383 DAL80 + 0.361 AZF1
YGL150C(INO80) 16.71 -21.43 0.21 YGL150C = -0.237 + 0.197 CST6 + 0.368 GAT3 + 0.169 KRE33 + 0.185 ABF1 + -0.122 CAD1
YDR193W(NA) 10.67 -13.35 0.48 YDR193W = 0.044 + 0.731 CST6 + -0.141 IFH1 + -0.185 DOT6
YAL061W(NA) 21.24 -28.47 0.02 YAL061W = -0.147 + -1.189 CST6 + 0.321 FKH1 + -.369 IXR1+1.521 BYE1+.125 GAT3 +.165 ACA1
YKL150W(MCR1) 12.29 -16.57 0.41 YKL150W = 0.048 + 0.515 ACA1 + -0.222 HIR1 + -0.205 GAL80
YDR515W(SLF1) 19.88 -29.76 0.09 YDR515W = 0.087 + 2.080 CST6 + -0.190 IFH1 + -2.660 GTS1 + 0.956 FHL1
  1. Genes fitted by the SDE model with beta sigmoid as regulatory function.