Convolutional Neural Network-based Sequence-to-Expression Prediction Tool (CoNSEPT)

Architecture of CoNSEPT. First the input enhancers are scanned with user-defined PWMs and passed to a pooling layer to extract the strongest matches. Next, TF profiles are integrated (multiplied) with the extracted scores and pair-wise features are built according to the user-defined TF–TF interactions. Each pair-wise feature is passed to a separate kernel capturing short-range regulatory interactions between pair of TFs. (Note: a TF pair may be homotypic.) The concatenated output of interaction convolutional kernels is passed to a sequence of activation functions (⁠σ⁠) and optional additional convolutional layers. The final activated output of the convolutional layers is passed to a dropout layer followed by a fully-connected layer and an activation function to predict the expression.