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GEMimp:微生物组数据的准确和强大的推算方法,使用嵌入神经网络的图形.

Ziwei Sun1, Kai Song1

  • 1School of Mathematics and Statistics, Qingdao University, Qingdao, China.

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|November 3, 2024
PubMed
概括
此摘要是机器生成的。

GEMimp有效地解决了微生物组数据的稀疏性,通过赋值缺失的值,改进疾病分类检测和增强微生物数据分析以获得更好的科学结论.

关键词:
图形嵌入神经网络的神经网络.归算是指指责一个人的行为.微生物组是一个微生物组.

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科学领域:

  • 微生物学 微生物学
  • 生物信息学是一种生物信息学.
  • 计算生物学 计算生物学

背景情况:

  • 微生物组研究强调了微生物组成与人类健康/疾病之间的联系.
  • 微生物组数据分析受到稀疏性 (零计数的流行) 的挑战.
  • 数据稀缺性可能会扭曲丰度分布,并降低发现的可靠性.

研究的目的:

  • 引入GEMimp,一种赋值方法,以提高微生物组数据的稳定性.
  • 评估GEMimp的性能与现有的最先进的归算技术相比.

主要方法:

  • 对于随机步行,GEMimp使用 node2vec 算法与宽度首次搜索 (BFS) 和深度首次搜索 (DFS).
  • 这种方法学习了分类学单位的低维表示,以重建相似性网络.
  • 对模拟和真实世界数据集进行的比较分析包括SAVER,MAGIC和mbImpute.

主要成果:

  • 与原始数据相比,GEMimp实现了最高的皮尔森相关系数,优于其他方法.
  • GEMimp在识别重要和与疾病相关的分类系统方面表现出熟练.
  • 该方法有效地缓解了模拟和真实世界微生物组数据集 (例如T2D,CRC) 的稀疏性问题.

结论:

  • GEMimp显著改善了稀疏微生物组数据的分析.
  • 通过减轻稀缺性,GEMimp促进了下游分析,并推动了微生物学研究.
  • 该方法增强了与疾病相关的微生物种群的检测.