动机细胞通过调节基因表达来对环境做出反应，以最佳地利用资源。技术的最新进展允许测量转录物、蛋白质、脂质和代谢物的丰度。这些高度复杂的数据集反映了生物系统中不同层的状态。多组学是这些不同方法和数据的整合，以获得更清晰的生物状态图。随着质谱技术继续普及，蛋白质组和代谢组的多组学研究变得越来越普遍。然而，通过整合这些数据来提取知识仍然具有挑战性。结果通过机器学习和模型解释的结合，发现了不同组学层中分子之间的联系。发现的联系反映了蛋白质对代谢物的控制。发现控制柠檬酸盐的蛋白质被映射到已知的遗传和代谢网络，表明这些蛋白质调节剂是新的。此外，对所有代谢物的蛋白质控制程度进行聚类可以预测五种基因功能，每一种都经过实验验证。两个未表征的基因 YJR120W 和 YLD157C 被准确预测为调节线粒体翻译。还预测和验证了三个未完全表征的基因的功能，包括 SDH9、ISC1 和 FMP52。网站可以进行结果探索以及用户提供的多组学数据的 MIMaL 分析。可用性 MIMaL 的网站位于 https://mimal.app 该网站的代码位于 https://github。com/qdickinson/mimal-website 实施 MIMaL 的代码位于 https://github.com/jessegmeyerlab/MIMaL 补充信息 补充数据可在 Bioinformatics 在线获得。支持数据可在 https://doi.org/10.5281/zenodo.6537297 获得 MS 数据在标识符 MSV000090100 下可在 https://massive.ucsd.edu/ProteoSAFe/dataset.jsp?task=ba70b1440b2b4c488323fa6644b332cb 获得 Motivation Cells respond to environments by regulating gene expression to exploit resources optimally. Recent advances in technologies allow measuring the abundances of transcripts, proteins, lipids and metabolites. These highly complex datasets reflect the state of the different layers in a biological system. Multi-omics is the integration of these disparate methods and data to gain a clearer picture of the biological state. Multi-omic studies of the proteome and metabolome are becoming more common as mass spectrometry technology continues to be democratized. However, knowledge extraction through integration of these data remains challenging. Results Connections between molecules in different omic layers were discovered through a combination of machine learning and model interpretation. Discovered connections reflected protein control over metabolites. Proteins discovered to control citrate were mapped onto known genetic and metabolic networks, revealing that these protein regulators are novel. Further, clustering the magnitudes of protein control over all metabolites enabled prediction of five gene functions, each of which was validated experimentally. Two uncharacterized genes, YJR120W and YLD157C, were accurately predicted to modulate mitochondrial translation. Functions for three incompletely characterized genes were also predicted and validated, including SDH9, ISC1, and FMP52. A website enables results exploration and also MIMaL analysis of user-supplied multi-omic data. Availability The website for MIMaL is at https://mimal.app Code for the website is at https://github.com/qdickinson/mimal-website Code to implement MIMaL is at https://github.com/jessegmeyerlab/MIMaL Supplementary information Supplementary figures are available at Bioinformatics online. Supporting data are available at https://doi.org/10.5281/zenodo.6537297 MS data are available under the identifier MSV000090100 at https://massive.ucsd.edu/ProteoSAFe/dataset.jsp?task=ba70b1440b2b4c488323fa6644b332cb