预测代谢物的途径参与,无论是途径类别还是个别途径
Erik D Huckvale1,2, Hunter N B Moseley1,2,3,4,5,6
1Markey Cancer Center, University of Kentucky, Lexington, KY, USA.
bioRxiv : the preprint server for biology
|August 16, 2024
概括
这项研究引入了一种新的机器学习模型,用于预测代谢物与特定代谢途径的相关性. 该模型实现了高精度,改进了现有的代谢途径注释方法.
科学领域:
- 生物化学和生物信息学
- 计算生物学 计算生物学
- 代谢学 代谢学 代谢学
背景情况:
- 代谢包括细胞生命所必需的相互连接的化学反应,这些反应被组织成代谢途径.
- 像KEGG这样的代谢知识库提供了宝贵的数据,但在代谢途径注释方面是不完整的.
- 现有的机器学习模型已经部分解决了使用化学结构的代谢物通路预测,主要用于更广泛的类别.
研究的目的:
- 开发和验证第一个能够预测代谢物与粒度KEGG3级代谢途径相关的机器学习模型.
- 为了提高代谢产物代谢途径注释的准确性和完整性.
- 为了建立一个新的基准对代谢物通路预测性能.
主要方法:
- 使用特征和数据集工程方法创建了一个大数据集,超过100万个代谢途径条目.
- 在这个工程数据集上训练了一个单一的二进制分类器.
- 该模型的性能被严格评估,使用100次交叉验证代,计算马修斯相关系数 (MCC).
主要成果:
- 机器学习模型在100次交叉验证代中实现了0.806 ± 0.017的平均MCC,用于预测3级路径.
- 172个3级路径的预测准确性导致整体MCC为0.726.
- 该模型在预测2级途径类别方面表现强,MCC为0.891,表明从3级预测中成功转移学习.
结论:
- 开发的机器学习模型在预测代谢物与代谢途径的相关性方面取得了重大进展,特别是在详细的KEGG 3级.
- 高预测准确度超过了该领域的先前结果,提供了对代谢网络的更全面的理解.
- 这项工作为旨在在特定代谢途径内注释代谢物的研究人员提供了一个强大的工具,推动了代谢学和系统生物学研究.
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