Multi-Omic Integration by Machine Learning (MIMaL) Reveals Protein-Metabolite Connections and New Gene Functions

Metabolomics and proteomics generate large, complex datasets that reflect the state of 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. Here we show that connections between these omic layers can be discovered through a combination of machine learning and model interpretation. We find that SHAP values connecting proteins to metabolites are valid experimentally, and reveal also largely new connections. Further, clustering the magnitudes of protein control over all metabolites enabled prediction of gene five gene functions, each of which was validated experimentally. We accurately predicted that two uncharacterized genes in yeast modulate mitochondrial translation, YJR120W and YLD157C.We also predict and validate functions for several incompletely characterized genes, including SDH9, ISC1, and FMP52. Our work demonstrates that multi-omic analysis with machine learning (MIMaL) is a new lens that reveals new insight from multi-omic data that would not be possible using any omic layer alone.