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一个合作学习框架,用于整合来自多个队伍的代谢学数据和共同的表型识别

E Salanon¹, E Jules¹, B Comte¹

¹University Clermont Auvergne, INRAE, UNH, Plateforme d'Exploration du Métabolisme, MetaboHUB Clermont, Clermont-Ferrand, France.

Computational and structural biotechnology journal

|January 22, 2026

概括

整合多个新陈代谢数据集可以提高生物标志物的发现. 一个新的合作学习框架平衡了早期和晚期的融合方法,减轻了批量效应,提高了跨研究的生物标志物可靠性.

科学领域:

* 计算生物学和生物信息学
* Omics数据整合和分析

背景情况:

*综合来自多项研究的代谢学数据增加了生物标志物发现的统计能力.
*挑战包括批量效应,研究特定偏差和数据集异质性,阻碍可重现性.
*现有的早期和晚期融合方法在处理研究间的变化和关系方面存在局限性.

研究的目的:

* 提出一个新的合作学习框架,用于多项研究的代谢学数据集成.
*通过平衡早期和晚期核聚变优势来改善候选生物标记物的发现.
*为了减轻研究特定的混因素,提高结果的可靠性.

主要方法:

* 一个合作式学习框架,将单变量和多变量分析与优化损失函数集成在一起.
*早期的整合使用了多块方法 (MINT-PLS-DA).
* 晚期聚变采用单独的PLS-DA,单变量分析采用混合模型.

主要成果:

* 该框架使用合成和现实世界的非定向代谢学人类数据集进行了验证.
*初步评估显示,批量效应减少,早期和晚期核聚变输出之间的良好一致性.
*现实世界数据分析显示,融合方法中的特征稳定性为10%,显示出更好的一致性.

结论:

*合作式学习方法有效地捕获跨数据集的共同表型信息.
* 它利用早期和晚期聚变的互补优势来实现稳健的代谢学数据集成.
* 开发的模型为可靠的潜在生物标志物发现提供了有效的解决方案.

关键词:

一群人群的队列.合作学习学习合作学习.数据整合数据集成

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