PopGenAdapt:半监督的域调整,用于在代表性不足的人口中进行基因型到表型预测
Marçal Comajoan Cara1,2, Daniel Mas Montserrat1, Alexander G Ioannidis1,3,4
1Dept. of Biomedical Data Science, Stanford University School of Medicine, Stanford, CA, USA.
bioRxiv : the preprint server for biology
|October 24, 2023
概括
基因组数据集缺乏多样性,阻碍了包容性的生物医学模型. PopGenAdapt使用半监督的域适应来改善代表性不足的人群中的基因型到表型预测.
科学领域:
- 基因组学就是基因组学.
- 生物医学信息学 生物医学信息学
- 机器学习 机器学习
背景情况:
- 基因组数据集主要来自欧洲祖先,限制了包容性生物医学模型的发展.
- 对代表性不足的人群而言,与电子健康记录相关的标记基因组数据稀缺.
- 这种数据差距阻碍了精准医学的公平进步.
研究的目的:
- 为了介绍PopGenAdapt,一个新的基因型到表型预测模型.
- 用半监督域适应 (SSDA) 来解决代表性不足人群中有限的标记数据的挑战.
- 提高机器学习模型在生物医学研究中的包容性和性能.
主要方法:
- PopGenAdapt采用半监督域适应 (SSDA) 技术,最初来自计算机视觉.
- 该模型利用来自欧洲祖先队伍的丰富标记数据和来自代表性不足的群体的有限/未标记数据.
- 对尼日利亚,斯里兰卡和夏威夷人群的疾病结果预测的表现进行了评估.
主要成果:
- PopGenAdapt在代表性不足的人群中,在基因型-表型预测准确度方面取得了显著的改进.
- 该模型在这些多样化的群体中表现优于现有的最先进的监督学习方法.
- SSDA在弥合数据稀缺造成的绩效差距方面被证明是有效的.
结论:
- 半监督域适应 (SSDA) 是增强基因组数据多样性的有希望的策略.
- PopGenAdapt为生物医学研究中更公平,更包容的机器学习应用提供了一条途径.
- 这些发现支持SSDA的更广泛应用,以改善不同祖先的健康结果.
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