Fig. 1

Framework of HPOseq. A Prediction based on intra-sequence features: based on amino acid sequence coding, intra-sequence features are first extracted using a multi-scale convolutional layer, followed by feature dimensionality reduction through pooling and spreading layers, and finally input into a fully connected layer to predict intra-sequence disease phenotypic association scores. B Prediction based on inter-sequence features: inter-sequence similarity among proteins is computed through the BLAST tool, and an attribute graph is constructed, followed by a variogram self-encoder to extract the node feature representations and input them into the fully connected neural network to predict the disease phenotype association scores between sequences. C Combination module: The fully connected neural network and mask matrix are used to fuse the prediction results under the intra- and inter-sequence feature sub-models to generate more accurate disease phenotype association scores