Skip to main content

Table 4 Results achieved by predictors in real datasets experiments

From: Sequence motif finder using memetic algorithm

Dataset Predictor Precision Recall F-Score
CREB MFMD 0.647±0.024 0.578±0.044 0.611±0.031
  MEME 0 0 0
  GIBBS 0.529 0.473 0.500
CRP MFMD 0.909±0.039 0.833±0.033 0.869±0.027
  MEME 0.904 0.791 0.844
  GIBBS 0.941 0.666 0.780
HNF1 MFMD 0.772±0.013 0.629±0.032 0.693±0.019
  MEME 0.136 0.111 0.122
  GIBBS 0.500 0.222 0.307
MCB MFMD 0.999±0.030 0.667±0.042 0.800±0.030
  MEME 0.692 0.750 0.719
  GIBBS 0.750 0.750 0.750
MEF2 MFMD 0.700±0.033 0.823±0.030 0.756±0.024
  MEME 0.705 0.705 0.705
  GIBBS 0.176 0.176 0.176
MYOD MFMD 0.363±0.016 0.380±0.024 0.372±0.018
  MEME 0.235 0.190 0.210
  GIBBS 0.208 0.238 0.222
NFKB MFMD 0.667±0.040 0.500±0.099 0.571±0.062
  MEME 0 0 0
  GIBBS 0.667 0.500 0.571
PDR3 MFMD 0.850±0.035 0.944±0.046 0.894±0.034
  MEME 0.653 0.944 0.772
  GIBBS 0.928 0.722 0.812
REB1 MFMD 0.800±0.027 0.600±0.025 0.685±0.021
  MEME 0.333 0.350 0.341
  GIBBS 0.266 0.200 0.228
SRF MFMD 0.477±0.007 0.583±0.014 0.525±0.008
  MEME 0.440 0.611 0.511
  GIBBS 0.514 0.500 0.507
TBP MFMD 0.657±0.004 0.768±0.008 0.708±0.006
  MEME 0.578 0.578 0.578
  GIBBS 0.308 0.347 0.326
  1. Some predictors failed to score in these experiments because they found initial positions with a deviation greater than 2. These data are highlighted in bold
\