Supplementary Figure 1: An extended version of Figure 2a, depicting multi-model training and reverse-complement mode
From: Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning
To use the GPU’s full computational power, we train several independent models in parallel on the same data, each with different calibration parameters. The calibration parameters with validation performance are used to train the final model. Shown is an example with batch_size=5, motif_len=6, num_motifs=4, num_models=3. Sequences are padded with ‘N’s so that the motif scan operation can find detections at both extremities. Yellow cells represent the reverse complement of the input located above; both strands are fed to the model, and the strand with the maximum score is used for the output prediction (the max strand stage). The output dimension of the pool stage, depicted as num_motifs (*), depends on whether “max” or “max and avg” pooling was used.
