Fig. 1.

The architecture of the recurrent neural network to train variant pathogenicity based on protein sequences. (a) The feature extractor contains two parallel layers of long short-term memory (LSTM) networks. The features of the wild-type sequence, mutated sequence and MSA are merged and processed to become a vector, known as the extracted features. (b) The pathogenicity classifier is composed of two fully connected layers that determine the binary pathogenicity of a variant from the extracted features. (c) The pathogenicity classifier utilizes SNVBox features and the extracted features by combining these two features through sigmoid activation followed by concatenation