EnTao-GPM: DNA Foundation Model for Predicting the Germline Pathogenic Mutations
This work addresses the problem of accurate genetic mutation interpretation for clinical diagnostics and research in precision medicine, representing a domain-specific advancement.
The paper tackled the challenge of distinguishing pathogenic from benign mutations in DNA by developing EnTao-GPM, a foundation model that achieved superior accuracy in mutation classification when validated against ClinVar.
Distinguishing pathogenic mutations from benign polymorphisms remains a critical challenge in precision medicine. EnTao-GPM, developed by Fudan University and BioMap, addresses this through three innovations: (1) Cross-species targeted pre-training on disease-relevant mammalian genomes (human, pig, mouse), leveraging evolutionary conservation to enhance interpretation of pathogenic motifs, particularly in non-coding regions; (2) Germline mutation specialization via fine-tuning on ClinVar and HGMD, improving accuracy for both SNVs and non-SNVs; (3) Interpretable clinical framework integrating DNA sequence embeddings with LLM-based statistical explanations to provide actionable insights. Validated against ClinVar, EnTao-GPM demonstrates superior accuracy in mutation classification. It revolutionizes genetic testing by enabling faster, more accurate, and accessible interpretation for clinical diagnostics (e.g., variant assessment, risk identification, personalized treatment) and research, advancing personalized medicine.