基于跨物种预训练的DNA模型，提升胚系致病突变预测准确率。
EnTao-GPM: DNA Foundation Model for Predicting the Germline Pathogenic Mutations
- 利用人类、猪、小鼠基因组跨物种预训练，增强非编码区致病信号识别
- 在ClinVar和HGMD上微调，对点突变和非点突变分类准确率更优
- 结合大模型生成解释，提供可读的临床决策支持，适合遗传诊断研究
区分致病突变与良性多态性仍是精准医学中的关键挑战。由复旦大学与BioMap联合开发的EnTao-GPM通过三项创新应对：(1) 在人类、猪、小鼠等哺乳动物基因组上进行疾病相关序列的跨物种靶向预训练，利用进化保守性增强对致病基序（尤其非编码区）的解析能力；(2) 通过在ClinVar和HGMD上微调，实现对SNVs与非SNVs的精准分类；(3) 构建可解释的临床框架，将DNA序列嵌入与大模型生成的统计解释相结合，输出可操作的临床洞察。经ClinVar验证，EnTao-GPM在突变分类任务中表现优于现有方法，推动遗传检测向更快、更准、更易用的方向发展，助力临床变异评估、风险识别及个性化治疗，加速精准医学进程。
原文摘要 · Abstract (English)
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.
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