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Erik D Huckvale¹, Hunter N B Moseley^2,3,4,5,6

¹Markey Cancer Center, University of Kentucky, Lexington, KY, USA.

BMC bioinformatics

|January 6, 2026

概括

机器学习模型有效地使用MetaCyc数据库预测生化路径,实现与KEGG可比的性能. 这增强了生物研究中的代谢物解释.

科学领域:

生物化学生物化学
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:

代谢物与生化途径的关联对于解释分子数据至关重要.
路径注释通常稀疏,限制了数据的实用性.
以前的机器学习模型使用京都基因和基因组百科全书 (KEGG) 预测了路径关联.

研究的目的:

用MetaCyc数据库评估机器学习模型的性能,以预测使用MetaCyc数据库的代谢途径关联.
将基于MetaCyc的预测与KEGG的预测进行比较.
评估MetaCyc对通路层次解释的有用性.

主要方法:

经过训练和评估的多层感知子模型.
使用了MetaCyc数据库中的化合物条目和路径注释.
将性能指标 (马修斯相关系数) 与在KEGG数据上训练的模型进行比较.

主要成果:

在MetaCyc上训练的模型获得了0.845 (±0.0101) 的平均MCC.
在KEGG上训练的模型获得了0.847 (±0.0098) 的平均MCC.
与KEGG的仅代谢途径相比,MetaCyc在代谢途径预测性能上显示了>5.6%的改善.

结论:

机器学习模型在使用MetaCyc和KEGG的路径预测方面表现相似.
MetaCyc提供了一套更全面的路径定义,用于预测.
这些发现支持MetaCyc有效地用于代谢途径关联预测.

关键词:

生物化学生物化学机器学习是机器学习.代谢物的代谢物.神经网络的神经网络的神经网络路径路径路径

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