来自机器学习方法的分类器特征集的路径空间距离评估协议
Bahar Tercan1, Victor H Apolonio2, Vinicius S Chagas2
1Institute of Systems Biology, 401 Terry Avenue North, Seattle, WA 98109, USA.
STAR protocols
|March 19, 2025
概括
这项研究提出了一种新的协议,用于协调通过不同的机器学习 (ML) 方法识别的基因特征,解决基因表达数据分析中的挑战. 路径空间R包有助于测试不同的特征集是否在路径空间中相关.
科学领域:
- 生物信息学是一种生物信息学.
- 计算生物学 计算生物学
- 机器学习 机器学习
背景情况:
- 模块中的基因共同调节使得机器学习 (ML) 中对基因表达数据的特征选择变得复杂.
- 由于复杂的监管网络,在不同的ML方法中识别强大的基因特征是具有挑战性的.
研究的目的:
- 提出一项协议,用于协调各种ML方法识别的不同分类器特征.
- 为评估在通路空间内看似不同的基因特征集之间的关系提供一种方法.
主要方法:
- 使用PathwaySpace R包进行基因表达数据分析.
- 准备输入数据并为基因组生成密度图.
- 在路径空间中测试特征集连接性的实施程序.
主要成果:
- 该协议使不同ML方法中的分类器特征能够协调.
- 它有助于测试不同基因组之间的关系在路径上下文.
结论:
- 本协议提供了一种系统的方法来整合和解释ML衍生的基因签名.
- 这种方法有助于理解基因共同调节,并提高了生物信息学特征选择的稳定性.
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