PhenoLinker: Phenotype-Gene Link Prediction and Explanation using
  Heterogeneous Graph Neural Networks
Jose L. Mellina Andreu Luis Bernal Antonio F. Skarmeta Mina Ryten Sara Álvarez Alejandro Cisterna García Juan A. Botía
The association of a given human phenotype to a genetic variant remains a
critical challenge for biology. We present a novel system called PhenoLinker
capable of associating a score to a phenotype-gene relationship by using
heterogeneous information networks and a convolutional neural network-based
model for graphs, which can provide an explanation for the predictions. This
system can aid in the discovery of new associations and in the understanding of
the consequences of human genetic variation.