Abstract: Methods and systems for treatment prioritization using a neural network model are provided. Training the model includes generating an enriched genetic knowledge graph with edges characterizing relationships between genetic variants, genes, diseases, treatments and symptoms, embedding the graph vertices in genetic knowledge vectors, receiving training patient feature value sets, generating a training dataset of training patient vectors labelled with target values comprising treatment efficacity by scaling the genetic knowledge vectors by the patient feature value sets, and training the model to process an input patient vector and generate a predicted efficacity of the at least one treatment option.

Abstract: Methods and systems for phenotyping based on a neural network model are provided. Training the model includes generating a dataset of individual-specific variant graphs associated representing dependencies among variants in individuals' genomes based on biomedical domain knowledge labelled according to target phenotype values to be predicted, and training the model using the training dataset to generate a phenotype prediction from an input individual-specific variant graph. Measure the contribution of a genetic variant to a phenotype of a target individual includes generating an individual-specific variant graph for the individual, modifying the individual-specific variant graph to remove a genetic variant, providing the initial and the modified individual-specific variant graphs as input to the phenotyping neural network model to generate two phenotype predictions, and calculating a difference between the two predictions, providing a measure of the contribution of the genetic variant to the phenotype.