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多尺度变异自编码器用于使用全基因组测序数据在非目标代谢学中赋值缺失的值
Chen Zhao1, Kuan-Jui Su2, Chong Wu3
1Department of Computer Science, Kennesaw State University, 680 Arntson Dr, Marietta, GA, 30060, USA.
Computers in biology and medicine
|July 2, 2024
概括
这项研究引入了一种使用全基因组测序 (WGS) 数据的新方法,以准确地归纳缺少的代谢学数据,改进分析和精准医学研究.
科学领域:
- 计算生物学 计算生物学
- 基因组学就是基因组学.
- 代谢学 代谢学 代谢学
背景情况:
- 缺少的数据是基于质谱的代谢学的一个重大挑战,可能导致有偏见和不完整的分析.
- 整合全基因组测序 (WGS) 与代谢学数据提供了一个有前途的策略,以提高数据归算的准确性.
研究的目的:
- 开发一种新的方法,通过利用全基因组测序 (WGS) 信息,在代谢学数据中归因未知的代谢物.
- 通过与基因组数据的整合,提高代谢学数据集的准确性和完整性.
主要方法:
- 采用多尺度变异自编码器,共同建模负担得分,多基因风险得分 (PGS) 和链接不平衡 (LD) 修剪单核酸多态 (SNP).
- 这种方法通过从两个omics数据集中学习潜伏表示来促进缺失的代谢学数据的特征提取和归算.
- 该方法通过利用基因组信息来赋予缺失的代谢学值.
主要成果:
- 拟议的方法证明了在缺乏值的经验代谢学数据集上比传统的归算技术更优异的性能.
- 使用35个模板代谢物,负担得分,PGS和LD修剪的SNP,该方法在71.55%的代谢物中获得了R2得分>0.01.
结论:
- 将WGS数据集成到代谢学归算中可以提高数据的完整性和下游分析,从而更准确地调查代谢途径和疾病关联.
- 这些发现强调了使用WGS数据用于代谢学归算的好处,以及在精准医学中多模式数据集成的重要性.
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