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公平的分子特征选择揭示了结直肠癌中普遍存在的瘤血统-信息性甲基化位点

Xuan Cindy Li^1,2, Yuelin Liu^1,3, Alejandro A Schäffer¹

¹Cancer Data Science Laboratory, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, 20892, United States.

Bioinformatics (Oxford, England)

|July 15, 2025

概括

我们开发了FALAFL,这是一个公平的多样本特征选择方法,用于测序数据. 它在各种结直肠癌患者中确定了信息化的CpG站点,帮助精准医学和公平的数据科学.

科学领域:

计算生物学计算生物学
基因组学就是基因组学.
生物信息学是一种生物信息学.

背景情况:

精准医学需要对不同患者群体进行比较分析.
在多患者研究中,分子特征的公平选择往往被忽视.

研究的目的:

介绍FAIR muLti-sample特征选择 (FAIR muLti-sample Feature seLection),这是一个在测序数据中进行平衡特征选择的算法.
解决不同患者队伍中公平选择分子特征的挑战.

主要方法:

开发了FALAFL,一种基于组合优化的算法方法.
应用FALAFL来选择结直肠癌单细胞甲基化测序数据中的谱系信息CpG位点.

主要成果:

在大多数患者中,FALAFL 快速而稳健地识别出最佳的 CpG 位点,覆盖率高.
选择的CpG位点在各种患者个人资料中显示出强大的瘤血统信息性.
在CpG岛屿地区之间,具有普遍信息性的CpG网站得到了丰富.

结论:

FALAFL 在设计诊断和预后面板方面提供帮助.
该方法在复杂疾病探索中促进公平的数据科学实践.
FALAFL确保在一个队列中的所有患者样本中进行平衡的特征选择.

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