将Omics数据集成到基因组规模的代谢建模中:准确医学的方法视角
Partho Sen1,2, Matej Orešič1,2
1Turku Bioscience Centre, University of Turku and Åbo Akademi University, FI-20520 Turku, Finland.
Metabolites
|July 29, 2023
概括
基因组规模代谢模型 (GEMs) 整合了omics数据以获得生物学见解. 本综述讨论了挑战和机器学习应用,以加强精准医学中的GEM和了解人类健康.
科学领域:
- 系统生物学和计算生物学.
- 代谢建模和生物信息学.
- 人类新陈代谢和微生物组研究.
背景情况:
- 奥米克技术产生了庞大的生物数据集.
- 将omics数据集成到数学模型中对于生物洞察至关重要.
- 基因组规模代谢模型 (GEMs) 是研究生物系统的强大工具.
研究的目的:
- 审查GEMs对理解人类新陈代谢和肠道微生物群的贡献.
- 讨论GEM可重现性和准确医学预测准确性的挑战.
- 探索机器学习在应对这些GEM挑战中的作用.
主要方法:
- 对基因组规模代谢模型 (GEMs) 的现有文献的审查.
- 对omics数据与GEMs集成的分析.
- 探索应用于代谢建模的机器学习技术.
主要成果:
- 基因基因组已经推进了对宿主微生物组代谢相互作用的理解.
- 关键的挑战包括确保GEM可重现性和提高预测准确性.
- 机器学习为增强GEM提供了有前途的解决方案.
结论:
- 将omics数据与GEM集成为新的生物发现提供了显著的潜力.
- 应对当前的挑战将提高GEM在精准医学中的实用性.
- 对机器学习应用的进一步研究将促进对健康和疾病中的分子机制的理解.
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