The paper introduces ChEA, a web-based tool that integrates genome-wide ChIP-X data to infer transcription factor (TF) regulation. ChEA uses a database of 189,933 interactions from 87 publications, describing the binding of 92 TFs to 31,932 target genes. The tool enables users to input gene lists and identify over-represented TF targets, revealing potential regulatory relationships. It also reconstructs a network of TFs based on shared targets and binding site proximity. Three case studies demonstrate ChEA's utility: (1) linking breast cancer signature genes to TFs, (2) re-analyzing gene over-expression in embryonic stem cells, and (3) combining ChEA with the Connectivity Map (CMAP) to design drug combinations targeting specific TFs in cancer. The first case study shows that SMAD2/3 is significantly enriched in breast cancer biomarker genes, highlighting its role in metastasis. The second case study reveals that Suz12, a polycomb group protein, is strongly associated with gene expression changes in mESCs, suggesting its role in differentiation. The third case study demonstrates how ChEA and CMAP can be combined to identify drug pairs that target Myc, a key oncogene. The paper emphasizes that integrating ChIP-X data with gene expression data improves the accuracy of TF regulation inference, offering a powerful tool for systems biology research. The ChEA database and software are freely available online.The paper introduces ChEA, a web-based tool that integrates genome-wide ChIP-X data to infer transcription factor (TF) regulation. ChEA uses a database of 189,933 interactions from 87 publications, describing the binding of 92 TFs to 31,932 target genes. The tool enables users to input gene lists and identify over-represented TF targets, revealing potential regulatory relationships. It also reconstructs a network of TFs based on shared targets and binding site proximity. Three case studies demonstrate ChEA's utility: (1) linking breast cancer signature genes to TFs, (2) re-analyzing gene over-expression in embryonic stem cells, and (3) combining ChEA with the Connectivity Map (CMAP) to design drug combinations targeting specific TFs in cancer. The first case study shows that SMAD2/3 is significantly enriched in breast cancer biomarker genes, highlighting its role in metastasis. The second case study reveals that Suz12, a polycomb group protein, is strongly associated with gene expression changes in mESCs, suggesting its role in differentiation. The third case study demonstrates how ChEA and CMAP can be combined to identify drug pairs that target Myc, a key oncogene. The paper emphasizes that integrating ChIP-X data with gene expression data improves the accuracy of TF regulation inference, offering a powerful tool for systems biology research. The ChEA database and software are freely available online.