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Table 2 List of genes reported as worst fitted in [6] and their prediction results from the SDE sigmoid model

From: A stochastic differential equation model for transcriptional regulatory networks

Target

logL

AIC

QE

Best Fit

YBR089W(NA)

-1.68

7.36

3.2

YBR089W = -0.166 + 0.367 HAA1

YDR285W(ZIP1)

0.77

2.46

3.69

YDR285W = 0.191 + -0.368 INO2

YFR057W(NA)

1.13

1.74

4.31

YFR057W = 0.098 + -0.188 GCN4

YAL018C(NA)

1.52

2.96

1.79

YAL018C = 0.055 + -0.303 IME1 + 0.195 CRZ1

YOR264W(DSE3)

2.26

-0.52

5.56

YOR264W = -0.059 + 0.129 ARG80

YOL116W(MSN1)

2.3

-0.59

3.77

YOL116W = -0.092 + 0.193 HAL9

YGR269W(NA)

2.4

-0.81

5.19

YGR269W = 0.097 + -0.194 HMS1

YOR383C(FIT3)

1.82

6.37

2.64

YOR383C = 0.367 + -0.287 ARG81 + -0.464 ECM22 + 0.412 GLN3 + -0.335 MAC1

YOR319W(HSH49)

2.17

5.65

4.92

YOR319W = 0.83 + -1.13 CIN5 + -0.655 FHL1 + 0.354 DAL81 + -0.275 FKH1

YKL001C(MET14)

2.58

-1.16

4.34

YKL001C = 0.091 + -0.18 IME1

YDL117W(CYK3)

2.59

-1.18

4.35

YDL117W = -0.162 + 0.359 AFT2

YKL185W(ASH1)

2.64

2.73

2.37

YKL185W = -0.150 + 0.407 ACE2 + -0.421 GAT1 + 0.302 INO2

YBR158W(AMN1)

2.65

8.7

1.2

YBR158W = -0.139 + 0.926 KRE33 + -0.941 IME4 + 0.571 MAL13 + 0.264 GAT3 + -0.347 CBF1 + -0.285 AZF1

YBR108W(NA)

2.66

-1.33

2.85

YBR108W = 0.112 + -0.205 HAC1

YAL020C(ATS1)

2.75

-1.51

4.15

YAL020C = -0.133 + 0.256 ASK10

YBR002C(RER2)

3.07

-2.14

2.26

YBR002C = 0.101 + -0.2 HAP5

YCL040W(GLK1)

3.09

-2.18

3.18

YCL040W = 0.095 + -0.199 HAL9

YNL018C(NA)

3.59

-3.18

2.19

YNL018C = 0.078 + -0.154 ARG81

YNL192W(CHS1)

3.21

1.57

2.13

YNL192W = -0.115 + 0.115 FZF1 + 0.306 DAL81 + -0.209 HMS2

YBR230C(NA)

3.32

3.37

2.2

YBR230C = -0.52 + 0.484 MAC1 + 0.467 GZF3 + 0.374 INO4 + -0.244 EDS1

  1. The set of the worst fitted 20 genes by the sigmoid model, sorted in the increasing order of the log-likelihood.