公平的机器学习可以抵消精准医学中的祖先偏见
Leslie A Smith1, James A Cahill2,3, Ji-Hyun Lee4,5
1Department of Computer & Information Science & Engineering, University of Florida, 1889 Museum Rd, Gainesville, 32611, FL, USA.
Nature communications
|March 11, 2025
概括
新型机器学习方法PhyloFrame解决了基因组数据中的祖先偏见. 这种方法提高了不同人群的预测准确性,促进了公平的精准医学和癌症基因发现.
科学领域:
- 基因组学就是基因组学.
- 生物信息学是一种生物信息学.
- 人工智能在医学中的应用
背景情况:
- 基因组数据集不成比例地代表了欧洲祖先,限制了对人类疾病的理解和公平的医学进步.
- 基因组数据中的祖先偏见阻碍了发现特定人群的疾病洞察力和治疗目标.
研究的目的:
- 介绍PhyloFrame,一种机器学习 (ML) 方法,旨在纠正基因组数据中的祖先偏差,以实现公平的精确医学.
- 提高基因组模型在不同祖先的预测能力和基因识别能力.
主要方法:
- PhyloFrame集成了功能互动网络和人口基因组学与转录组数据,以调整祖先偏差.
- 该方法应用于乳腺,甲状腺和子宫癌数据集,并在14个不同的基因组数据集中验证.
主要成果:
- PhyloFrame 证明了预测能力的提高,并减少了所有祖先的模型过拟合,特别是在代表性不足的群体.
- 该方法显示,识别已知癌症相关基因的可能性更高,并且由于与训练数据的基因学距离,有效地减轻了性能影响.
结论:
- 像PhyloFrame这样的公平的人工智能 (AI) 方法可以减轻基因组训练数据中的祖先偏见.
- 这些进展对于在医学研究中实现公平代表性和改善所有人口的医疗保健结果至关重要.
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