用XGBoost进行多标签分类,用于代谢途径预测
Hyunwhan Joe1, Hong-Gee Kim2,3
1Biomedical Knowledge Engineering Lab., Seoul National University, Seoul, Republic of Korea.
BMC bioinformatics
|January 31, 2024
概括
机器学习方法用于代谢途径预测,当应用分类修剪时,现在超过了传统方法,如PathoLogic. 一种新的基于XGBoost的方法mlXGPR,在单个生物体的基准指标上显示出优异的性能.
科学领域:
- 系统生物学 系统生物学
- 计算生物学是一种计算生物学.
- 基因组学就是基因组学.
背景情况:
- 代谢途径预测对于从基因组数据中重建生物体代谢网络至关重要.
- 机器学习 (ML) 方法显示出有希望的结果,但以前的基于规则的方法表现不佳,例如PathoLogic.
- 之前对PathoLogic的评估遗漏了分类学修剪,这对其性能产生了负面影响.
研究的目的:
- 重新评估PathoLogic与分类学修剪并将其与ML方法进行比较.
- 引入mlXGPR,一种改进的基于XGBoost的代谢途径预测方法.
- 通过通过分类链结合标签相关性来增强ML路径预测.
主要方法:
- 更新了PathoLogic的评估,包括分类学修剪.
- 使用XGBoost和多标签分类框架开发mlXGPR.
- 采用新的排名方法实施分类链,以利用标签相关性.
- 对单个和多个生物体数据集的现有方法进行mlXGPR的基准测试.
主要成果:
- 带有分类学修剪的病理学超越了以前的ML方法.
- mlXGPR,特别是使用分类器链,超过了PathoLogic和其他ML方法.
- 在单个生物基准上,mlXGPR在汉明损失,精度和F1得分方面实现了卓越的性能.
结论:
- 基于ML的代谢途径预测可以实现高性能.
- 优化的ML方法,如mlXGPR,可以超过已建立的工具,如PathoLogic与分类修剪.
- 对ML方法的进一步改进对于竞争性代谢途径预测是可行的.
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