关于深度学习的调查对多基因风险得分进行了调查
Max Schuran1, Benjamin Goudey2,3,4, Gillian S Dite1,5
1Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, 207 Bouverie St, Carlton, VIC 3053, Australia.
Briefings in bioinformatics
|August 13, 2025
概括
深度学习神经网络通过建模复杂的遗传相互作用来改善多基因风险得分 (PRS) 的前景. 需要进一步的研究和标准化的基准来充分发挥其在预测疾病风险方面的潜力.
科学领域:
- 遗传学 遗传学 是一个
- 计算生物学 计算生物学
- 人工智能的人工智能
背景情况:
- 多基因风险评分 (PRS) 使用多个遗传变异预测疾病倾向.
- 目前的PRS经常使用线性模型,限制了预测准确度,无法捕捉完整的遗传变异性.
- 深度学习神经网络为建模非线性遗传关系提供了潜力.
研究的目的:
- 对深度学习方法进行调查和分类,以建模多基因风险评分.
- 突出PRS中各种神经网络架构的假设,优点和弱点.
- 确定挑战,并建议基于深度学习的PRS开发的未来方向.
主要方法:
- 在多基因风险评分中对深度学习应用的文献调查.
- 神经网络架构的分类 (例如基于序列的,图形神经网络,自动编码器).
- 对建模假设,预测能力和可解释性的分析.
主要成果:
- 基于序列的模型,图形神经网络和生物信息网络显示出增强PRS预测能力的前景.
- 带有潜伏表示的自动编码器可以在不同的祖先中提高性能.
- 缺乏标准化的基准和解释性挑战阻碍了进步.
结论:
- 深度学习架构为提高多基因风险评分准确性提供了巨大的潜力.
- 建立报告标准和基准对于推进基于深度学习的PRS至关重要.
- 在从深度学习模型推断因果关系时,需要仔细考虑可解释性.
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