使用分子指纹探索机器学习的非目标代谢学
Christel Sirocchi1, Federica Biancucci2, Matteo Donati1
1Department of Pure and Applied Sciences, University of Urbino, Piazza della Repubblica, 13, Urbino, 61029, Italy.
Computer methods and programs in biomedicine
|April 16, 2024
概括
在代谢物指纹上的机器学习有助于分析复杂的代谢学数据. 这种方法揭示了新的代谢途径,并有助于理解已知的生物过程之外的细胞反应.
科学领域:
- 生物化学 生物化学
- 计算生物学 计算生物学
- 系统生物学 系统生物学
背景情况:
- 代谢学研究细胞代谢,提供对生物体状态的见解.
- 由于途径注释有限,分析大型代谢学数据集具有挑战性.
研究的目的:
- 将机器学习应用到代谢物指纹上,以探索代谢过程.
- 解决代谢学数据分析方面的挑战,包括数据稀疏性和可解释性.
主要方法:
- 在代谢物指纹上利用机器学习,灵感来自于药物发现技术.
- 评估指纹的有效性和应用特征的重要性分析,以解释性.
- 在与 Ataxia Telangiectasia 和内皮细胞相关的数据集上测试了该方法.
主要成果:
- 机器学习有效地使用分子指纹预测了代谢物反应.
- 功能重要性分析与已知的代谢途径保持一致.
- 确定了与实验条件相关的新型代谢物组.
结论:
- 该研究通过在代谢物指纹上采用机器学习来弥合药物发现和代谢学.
- 该方法增强了复杂的代谢学数据的分析,并有助于发现新的代谢见解.
- 这项工作为未来使用先进的计算方法进行代谢学研究提供了基础.
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