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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.