Machine Learning in Mammals' Health: Integrating Multi-Omics Data for Precision Nutrition and Sustainable Production

Precision nutrition in modern mammalian production aims to transition from static feeding regimens toward dynamic and customized strategies by unraveling complex interactions among genetics, environment, the gut microbiome, and host physiology. Machine learning provides a powerful analytical framework to navigate high-dimensional multi-omics datasets. However, existing applications frequently lack systematic criteria for algorithm selection and remain largely confined to observational statistical associations without progressing toward causal verification. This perspective delineates a coherent translational chain spanning three interconnected operational scenarios: key functional microbiota screening to identify whom to intervene on, multi-omics biomarker identification to define when to intervene, and host pathway characterization to reveal how the animal responds. We critically examine the operational boundaries, interpretability trade-offs, and failure modes of classical algorithms including LASSO, support vector machines, random forest, and graph neural networks across these domains. Furthermore, we address critical field bottlenecks such as high dimensionality, small-sample overfitting, batch effects, and black-box opacity, while highlighting emerging paradigms including multimodal data fusion, transfer learning, and causal machine learning to bridge the gap between data-driven screening and actionable nutritional interventions. Ultimately, transforming machine learning from an auxiliary predictive tool into an autonomous precision nutrition engine requires shifting the research focus from isolated model accuracy to reproducible and causally validated biological targets.