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图形随机森林:用于识别高度连接的重要特征的图形嵌入算法.

Leqi Tian1,2, Wenbin Wu1, Tianwei Yu1,2,3

  • 1School of Data Science, The Chinese University of Hong Kong, Shenzhen 518172, China.

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概括
此摘要是机器生成的。

图形随机森林 (GRF) 通过整合网络信息来改善生物数据中的基因选择. 这种方法识别了功能连接的重要基因,提高了可解释性,同时保持了分类准确性.

关键词:
功能选择 功能选择基因网络 基因网络随机的森林随机的森林

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科学领域:

  • 生物信息学是一种生物信息学.
  • 机器学习 机器学习
  • 计算生物学 计算生物学

背景情况:

  • 随机森林 (RF) 是一种强大的机器学习算法,有效用于分类和回归,特别是在样本大小有限的生物应用中.
  • 基因表达数据集往往是一个高维的挑战,具有显著更多的特征 (基因) 比样本 (p >> n).
  • 用于特征选择的标准RF可以产生分散的重要基因,这与功能基因网络一致性的生物原理相矛盾.

研究的目的:

  • 开发一种增强的特征选择方法,将生物网络拓学纳入随机森林算法.
  • 通过利用已知的生物网络来识别功能连接的重要基因.
  • 提高特征选择在高维生物数据中的可解释性.

主要方法:

  • 介绍了Graph Random Forest (GRF),这是一个新的算法,在森林建设过程中集成外部生物网络信息.
  • GRF识别了重要的特征,这些特征在生物网络中形成高度连接的子图.
  • 通过模拟实验验证并应用于现实世界RNA-seq数据集 (非小细胞肺癌和人类胚胎干细胞).

主要成果:

  • GRF实现了与标准RF相美的分类准确性.
  • 通过GRF识别的精选重要的基因在生物网络中表现出高度的连接性,形成可解释的子图.
  • 该方法在识别生物相关特征方面表现出有效性.

结论:

  • 图形随机森林 (GRF) 通过结合网络拓来提供一种有效的方法来选择生物数据中的特征.
  • 通过识别连接的基因子图,GRF增强了所选特征的生物解释性.
  • 拟议的方法是生物信息学中基于图形的分类和特征选择技术的宝贵补充.