预测反应体知识库中注释的化合物的途径参与
Erik D Huckvale1, Hunter N B Moseley1,2,3,4,5
1Markey Cancer Center, University of Kentucky, Lexington, KY 40536, USA.
Metabolites
|March 26, 2025
概括
预测未注释的生物分子的代谢途径可以改善代谢学分析. 使用Reactome知识库用于机器学习模型,与以前的方法相比,大大提高了路径预测的准确性.
科学领域:
- 代谢学 代谢学 代谢学
- 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:
- 小生物分子的路径注释对于解释代谢学数据至关重要.
- 有限的路径注释阻碍了全面的分析和omics集成.
- 以前的预测模型仅依赖于基因和基因组的京都百科全书 (KEGG) 数据集.
研究的目的:
- 开发和评估机器学习模型,用于预测未注释的生物分子的途径参与.
- 探索Reactome知识库对于路径预测的实用性.
- 改进代谢学数据解释和奥米学数据集成.
主要方法:
- 从Reactome知识库化合物构建了一个机器学习数据集.
- 工程化代谢物 - 途径配对特征向量.
- 训练并评估了一个多层感知子二进制分类器.
主要成果:
- 在Reactome数据上训练的模型比基于KEGG的模型 (0.847) 实现了0.916的更高的平均马修相关系数 (MCC).
- Reactome数据集包含的路径数量 (3985) 比KEGG数据集 (502) 的路径数量要多.
- 开发的模型证明了使用Reactome数据有效预测路径参与.
结论:
- Reactome知识库是开发准确路径预测模型的宝贵资源.
- 使用Reactome显著扩大了可预测的人类定义路径的数量.
- 这种方法增强了改善代谢学数据分析和OMIC集成的潜力.
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