Knowledge Graph Sparsification for GNN-based Rare Disease Diagnosis
This addresses the problem of prolonged and inaccessible diagnosis for rare disease patients, particularly in resource-limited settings, by enabling analysis with only phenotypic data.
The paper tackles rare genetic disease diagnosis by proposing RareNet, a Graph Neural Network that uses patient phenotypes to identify causal genes and retrieve subgraphs for clinical investigation, demonstrating competitive gene prediction and performance gains when integrated with other frameworks.
Rare genetic disease diagnosis faces critical challenges: insufficient patient data, inaccessible full genome sequencing, and the immense number of possible causative genes. These limitations cause prolonged diagnostic journeys, inappropriate treatments, and critical delays, disproportionately affecting patients in resource-limited settings where diagnostic tools are scarce. We propose RareNet, a subgraph-based Graph Neural Network that requires only patient phenotypes to identify the most likely causal gene and retrieve focused patient subgraphs for targeted clinical investigation. RareNet can function as a standalone method or serve as a pre-processing or post-processing filter for other candidate gene prioritization methods, consistently enhancing their performance while potentially enabling explainable insights. Through comprehensive evaluation on two biomedical datasets, we demonstrate competitive and robust causal gene prediction and significant performance gains when integrated with other frameworks. By requiring only phenotypic data, which is readily available in any clinical setting, RareNet democratizes access to sophisticated genetic analysis, offering particular value for underserved populations lacking advanced genomic infrastructure.