Fig. 1.
The proposed DNN uses Conv and LSTM layers. Peptide sequences are encoded into uniform numerical vectors of length 200. These vectors (X) are fed to an embedding layer of length 128, followed by a convolutional layer comprised of 64 filters. Each of these filters undergoes a 1D convolution and is downsampled via a maximal pooling layer of size 5. Next, an LSTM layer with 100 units allows the DNN to remember or ignore old information passed along the horizontal dotted arrows extending from each Xi input. The final output from the DNN is passed through a sigmoid function so that predictions (Y) are scaled between 0 and 1