Fig. 3

Simplified representation of model architecture. The model in this example was trained on 150 nt long sequences using all three available input types—sequence, secondary structure, and conservation score—each represented by a separate model branch. After the network extracts information from the separate inputs, the branches are concatenated, and the network continues learning interdependencies by looking at the combined information via dense or recurrent layers. Boxes represent individual layers, while the adjacent numbers indicate the data dimensionality. A plain graphical representation of the network architecture is produced and exported by ENNGene for every trained model