ADDITIONAL FILES

Additional file 1: The mathematical definitions of the four other similarity metrics, the workflow of the IDBC algorithm, an example feature vector, the mathematical definitions of five statistical variables to measure classifier performance, the default parameter values of classifiers in MATLAB, and histograms visualizing the first two criteria for selecting positives from the CRISPR-generated knockdown data.
Format: .docx

Additional file 2: The lists of positives and negatives in the machine learning classifiers to predict genes with similar tissue-wide expression profiles
Format: .xlsx

Additional file 3: The lists of positives and negatives in the DT classifier to predict TF targets based on the CRISPR-generated knockdown data
Format: .xlsx

Additional file 4: The lists of positives and negatives in the DT classifier to predict DE direct targets based on the siRNA-generated knockdown data
Format: .xlsx

Additional file 5: The performance of the DT classifier using only TFBS counts
Format: .xlsx

Additional file 6: The list of the most similar 500 PC genes to each TF in terms of tissue-wide expression profiles, and the intersection of these 500 genes and target genes of the TF
Format: .xlsx

Additional file 7: Cofactor binding sites adjacent to YY1 and EGR1 sites in the promoters of their targets and non-targets
Format: .docx

Additional file 8: The percentages of positives and negatives whose promoters do not overlap DHSs for the CRISPR-perturbed TFs
Format: .xlsx
