Table 2.
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 |
The set of the worst fitted 20 genes by the sigmoid model, sorted in the increasing order of the log-likelihood.