Recon8D:一种由八元组学和机器学习开发的代谢调节组网络
bioRxiv : the preprint server for biology
|September 4, 2024
概括
机器学习模型使用多omics数据预测癌细胞代谢组. 转录组学和特定的分子特征,如miRNA和基因组修饰,是关键预测因素,揭示了治疗点.
科学领域:
- 生物化学 生物化学
- 基因组学就是基因组学.
- 系统生物学 系统生物学
背景情况:
- 代谢调节网络是复杂和不完整的,阻碍了从其他omics数据中进行代谢预测.
- 了解这些网络对于癌症研究和治疗开发至关重要.
研究的目的:
- 使用机器学习和多omics数据预测癌症细胞系的代谢变异.
- 确定驱动癌症代谢变化的关键分子特征和途径.
主要方法:
- 利用机器学习整合基因组学,表观基因组学 (基因组PTM,DNA甲基化),转录基因组学,RNA剪接,miRNA-omics,蛋白质组学和光蛋白质组学数据来自1000个癌症细胞系.
- 重建了可预测代谢物的多原子相互作用子网络.
主要成果:
- 代谢组与转录组密切相关;miRNAs,蛋白和基因组PTM提供了每个特征的最多代谢信息.
- 周边代谢物是可以通过酶水平来预测的,而中心代谢物则需要来自信号和氧化还原通路的组合预测因素.
- 确定YAP1信号是四个欧米层的顶级全球预测器.
结论:
- 多omics数据可以有效地预测癌症细胞系的代谢变异.
- 确定了关键的分子预测因子和子网络,强调了YAP1信号作为一个重要因素.
- 优先预测高级代谢学分析的预测特征,并确定了包括合成致死相互作用在内的潜在治疗点.
相关概念视频
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These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
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