Deep Learning for diagnosing patients with rare genetic diseases

There are over 7,000 unique rare diseases, some of which affecting 3,500 or fewer patients in the US. Due to clinicians' limited experience with such diseases and the considerable heterogeneity of their clinical presentations, many patients with rare genetic diseases remain undiagnosed. While artificial intelligence has demonstrated success in assisting diagnosis, its success is usually contingent on the availability of large annotated datasets. Here, we present SHEPHERD, a deep learning approach for multi-faceted rare disease diagnosis. To overcome the limitations of supervised learning, SHEPHERD performs label-efficient training by (1) training exclusively on simulated rare disease patients without the use of any real labeled data and (2) incorporating external knowledge of known phenotype, gene and disease associations via knowledge-guided deep learning. This repository houses (1) the preprocessed rare disease knowledge graph, (2) the simulated patients used for training SHEPHERD, and (3) the myGene2 rare disease patients used for evaluation. The accompanying github repository can be found at: https://github.com/mims-harvard/SHEPHERD.