Fig. 1.

Siamese neural network architecture, composed of twin CNNs. The twin networks are joined at the final layer. The vectors a and b represent a pair of motifs from the training set, while h_a and h_b represent the respective hidden layers output by either CNN. The difference between the hidden layers is calculated to obtain the distance layer, D_w. D_w is input into the loss along with Y, a variable indicating the dissimilarity, regarding kinase interactions, between a and b. After training is complete, the so-called ‘twin’ architecture is no longer necessary; each motif is input into a single twin and the output of the embedding layer gives the resultant representation of the given motif