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, Daniel Mas Montserrat, Alexander G Ioannidis
1Dept. of Biomedical Data Science, Stanford University School of Medicine, Stanford, CA, USA2Dept. of Signal Theory & Communications, Universitat Politècnica de Catalunya, Barcelona, Spain.
Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
|December 31, 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使用半监督域适应 (SSDA) 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:
- 基因组数据集显示出显著的祖先偏见,主要包括欧洲祖先的个体.
- 这种缺乏多样性阻碍了公平和包容性的生物医学模型的发展.
- 与电子健康记录相关的标记基因组数据对于代表性不足的人群来说特别稀缺.
研究的目的:
- 为了介绍PopGenAdapt,一个新的基因型到表型预测模型.
- 为了解决基因组数据集中的数据稀缺性和祖先偏见.
- 为代表性不足的人群增强机器学习模型的性能.
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.
- 该模型利用了来自欧洲祖先队伍的现有标记数据.
- 它整合了来自代表性不足的人群的有限的标记和广泛的未标记数据.
主要成果:
- 在尼日利亚,斯里兰卡和夏威夷的代表性不足的人口中,PopGenAdapt在基因型-表型预测方面取得了显著的性能改善.
- 该模型的性能优于现有的最先进的监督学习方法.
- 在提高模型的包容性和准确性方面,SSDA被证明是有效的.
结论:
- 半监督域调整 (SSDA) 是开发生物医学研究中包容性机器学习模型的一个有希望的策略.
- 在基因组数据分析中,PopGenAdapt提供了一种可行的解决方案,以减轻祖先偏见.
- 这些发现为个性化医学的更公平的进步铺平了道路.
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