EnTao-GPM: DNA Foundation Model for Predicting the Germline Pathogenic Mutations
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866918107080032256 |
|---|---|
| author | Lin, Zekai Sun, Haoran Guo, Yucheng Yang, Yujie Wang, Yanwen Hu, Bozhen Ye, Chonghang Yang, Qirong Zhong, Fan Zhang, Xiaoming Liu, Lei |
| author_facet | Lin, Zekai Sun, Haoran Guo, Yucheng Yang, Yujie Wang, Yanwen Hu, Bozhen Ye, Chonghang Yang, Qirong Zhong, Fan Zhang, Xiaoming Liu, Lei |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_21706 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | EnTao-GPM: DNA Foundation Model for Predicting the Germline Pathogenic Mutations Lin, Zekai Sun, Haoran Guo, Yucheng Yang, Yujie Wang, Yanwen Hu, Bozhen Ye, Chonghang Yang, Qirong Zhong, Fan Zhang, Xiaoming Liu, Lei Genomics Artificial Intelligence 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. |
| title | EnTao-GPM: DNA Foundation Model for Predicting the Germline Pathogenic Mutations |
| topic | Genomics Artificial Intelligence |
| url | https://arxiv.org/abs/2507.21706 |