Fig. 6
From: Accurate prediction of cell type-specific transcription factor binding
Iterative training procedure. Starting from an initial set of negative regions and the complete set of positive regions, a first classifier is trained and applied to the training data, and putative false positive (i.e., “unbound” regions with large prediction scores) are identified. In each of the subsequent iterations, such regions are added to the set of negative regions, which are in turn used for training refined classifiers. The result of this iterative training procedure is a set of five classifiers trained in five cycles of the iterative training procedure