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Updated: Feb 15, 2026

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基于ACMG/AMP的变种分类工具的全面评估

Tohid Ghasemnejad1, Yuheng Liang1, Khadijeh Hoda Jahanian2

  • 1UNSW BioMedical Machine Learning Lab (BML), School of Biomedical Engineering, UNSW Sydney, Sydney, NSW 2052, Australia.

Bioinformatics (Oxford, England)
|February 13, 2026
PubMed
概括

本研究比较了用于孟德尔乱的ACMG/AMP变异解释工具. 像LIRICAL和Franklin这样的表型驱动工具在变体优先级方面显示出卓越的准确性.

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