A simple metric of promoter architecture robustly predicts expression breadth of human genes suggesting that most transcription factors are positive regulators

Table 5 SVM trained with data on the numbers of interacting Tfbs (SVM-Tfbs) improved on simple correlation, but adding data on GC-content (SVM-Tfbs + GC) did not lead to further improvement of predictions

NOTE: For SVM-Tfbs and SVM-Tfbs + GC three correlations were given: prediction (in bold – this is the result), scrambled (response vector was randomized when learning – this is a negative control), and retained (response vector was retained in the learning dataset – this is a positive control). SVM-Tfbs was trained with data on the numbers of interacting Tfbs only. SVM-Tfbs + GC training dataset additionally included data on promoter GC- and CpG-content.

ISSN: 1474-760X