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Table 2 The 11 compared existing methods.

From: Identifying cooperative transcription factors in yeast using multiple data sources

Existing methods Data sources used Method description Threshold of p-value Number of predicted cooperative TF pairs
Wang et al. (2006) [38] ChIP-chip data, gene expression data, TFBS data They developed a new framework to infer the combinatorial control of TFs by integrating heterogeneous functional genomic datasets. 10−3 14
Tsai et al. (2005) [5] ChIP-chip data, gene expression data They used statistical methods to identify yeast cell cycle TFs and synergistic pairs of TFs. 10−5 18
Elati et al. (2007) [28] Gene expression data They adopted a data mining system to learn transcriptional regulation relationship from gene expression data. 10−3 20
Nagamine et al. (2005) [8] ChIP-chipdata, PPI data They inferred the cooperative pairs under the assumption that the existence of interaction between two proteins suggests that they contribute to the same or similar biological process. 10−3 24
Datta and Zhao (2007) [2] ChIP-chip data They used a log-linear model to study cooperative binding among TFs and developed an Expectation-Maximization algorithm for statistical inferences. 10−3 25
He et al. (2006) [6] ChIP-chip data, gene expression data They adopted the microarray expression data to predict the cooperative TF pairs by testing whether the expression of target genes was significantly influenced by their cooperative effect with the multivariate method, ANOVA. 10−2 30
Banerjee and Zhang (2003) [4] ChIP-chip data, gene expression data They infer the cooperative pairs under the assumption that a pair of TFs is cooperative if genes regulated by both TFs are more co-expressed than those genes regulated by either TF alone. 5 × 10−2 31
Chang et al. (2006) [7] ChIP-chip data, gene expression data They employed a stochastic system model to assess TF cooperativity. 10−21 55
Yang et al. (2010) [9] ChIP-chip data, TF knockout data They predicted cooperativity between TFs by identifying the most statistically significant overlap of target genes regulated by two TFs in ChIP-chip data and TF knockout data. 5 × 10−3 186
Chen et al. (2012) [3] ChIP-chip data They facilitated identification of interactions between TFs by using motif discovery method when detecting overlapping targets of TFs based on ChIP-chip data. 10−3 221
Yu et al. (2006) [1] ChIP-chip data They proposed a method: Motif-PIE, which predicts interacting TF pairs by using a motif discovery procedure. 10−8 300
  1. The table shows the information of 11 existing methods adopted for comparison. The data sources used, a brief description of the method, the p-value threshold and the number of predicted cooperative TF pairs of each method are described in columns 2, 3, 4 and 5.