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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.