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Table 1 Comparing motif similarity scores of nine yeast TFs from four different calculations.

From: The effect of prior assumptions over the weights in BayesPI with application to study protein-DNA interactions from ChIP-based high-throughput data

TF Name (consensus sequence length) Activated in stress conditions BayesPI - Gaussian prior BayesPI - Laplace prior BayesPI - Cauchy prior MatrixREDUCE
ACE2 (6) No 0.89 0.95 0.96 0.90
MSN2 (6) Yes[17] 0.76 0.93 0.79 NA
SWI4 (7) No 0.96 0.94 0.94 0.95
YAP1 (7) Yes[18] 0.93 0.92 0.92 0.93
INO4 (8) No 0.90 0.92 0.94 0.97
SKN7 (9) Yes[19] 0.86 0.87 0.86 0.82
FHL1 (10) No 0.95 0.95 0.93 0.88
ROX1 (12) Yes[20] 0.72 0.72 0.78 0.75
XBP1 (12) Yes[21] 0.76 0.77 0.76 NA
  1. For the nine yeast TFs, the ChIP-chip datasets were obtained from [7]; the regularization constants α in BayesPI were divided into four classes; MatrixREDUCE program was downloaded from the publication [8] and its default parameters were used in the present study. Here, the motif similarity scores greater than 0.85 represents a good match between the prediction and the SGD consensus sequences [1]. Poor predictions are marked by bold text. NA indicates that no results are available owing to the program reason. All the programs were applied on the same datasets and were run under a PC cluster (a dual-core CPU SUN X6220 blade node with 16 GB of RAM).