Recent Advances in Integrating Machine Learning with Omics Approaches in Food Science and Nutrition Research

Abstract

Omics technologies are revolutionizing food and nutrition research by enabling high-throughput analysis of food components and microorganisms and revealing the intricate relationships between food and human health. Machine learning (ML) methods are particularly useful for classification, clustering, dimensionality reduction, and pattern detection in large data sets. The integration of multiomics data has driven the adoption of ML in developing predictive models for food processing, safety, and nutrition. This review examines genomics, transcriptomics, proteomics, metabolomics, and multiomics in food and nutrition research, along with applications of various ML techniques, including classical ML, deep learning (DL), and artificial neural networks (ANNs). The primary advantage of integrating ML with omics lies in its ability to analyze complex data sets, aiding food composition, quality assessment, and nutritional interventions. However, challenges in data quality, model reliability, and interpretability remain. This review updates the current state of ML-omics integration and outlines future directions in foodomics and nutriomics.