机器学习和基因组学交集的基础,临床和生物伦理研究的机会
Shurjo K Sen1, Eric D Green1, Carolyn M Hutter1
1National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892, USA.
Cell genomics
|January 8, 2024
概括
基因组学和机器学习 (ML) 正在融合,在数据集成,偏见减少,透明度和隐私方面面临挑战. 解决这些问题将推动ML在基因组研究中的应用.
科学领域:
- 基因组学就是基因组学.
- 机器学习 (ML) 是指机器学习.
- 生物信息学是一种生物信息学.
背景情况:
- 基因组学和ML正在融合,基因组学为ML算法开发提供了丰富的数据集.
- 机器学习为从复杂的基因组数据中提取知识提供了强大的工具.
研究的目的:
- 确定基因组学和ML交叉处的共同挑战.
- 收集关于支持ML在基因组研究中的建议.
主要方法:
- 与基因组学和ML领域的研究人员进行调查.
- 分析共同的挑战和提出的解决方案.
主要成果:
- 关键的挑战包括数据数量/多样性,数据集偏差,ML方法透明度/可解释性和数据隐私.
- 建议侧重于整合各种数据集 (多种经济学,细胞类型特定,健康的社会决定因素).
结论:
- 加强培训数据集,减轻偏见和确保透明度对于推动基因组学ML的发展至关重要.
- 开发保护隐私的ML技术对于伦理基因组研究至关重要.
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