Federated learning for the pathogenicity annotation of genetic variants in multi-site clinical settings.

Abstract excerpt

MOTIVATION: Rare diseases collectively affect 5% of the population. However, fewer than 50% of rare disease patients receive a molecular diagnosis after whole genome sequencing. Supervised machine learning is a valuable approach for the pathogenicity scoring of human genetic variants. However, existing methods are often trained on curated but limited central repositories, resulting in poor accuracy when tested on...

Topics

• Humans
• Genetic Variation
• Machine Learning
• Algorithms
• Molecular Sequence Annotation
• Rare Diseases