Multimodal deep learning as a next challenge in nutrition research: tailoring fermented dairy products based on cytidine diphosphate-diacylglycerol synthase-mediated lipid metabolism

Abstract

Deep learning is evolving in nutritional epidemiology to address challenges including precise nutrition and data-driven disease modeling. Fermented dairy products consumption as the implementation of specific dietary priority contributes to a lower risk of all-cause mortality, cardiovascular disease, and obesity. Various lipid types play different roles in cardiometabolic health and fermentation process changes the lipid profile in dairy products. Leveraging the power of multiple biological datasets can provide mechanistic insights into how proteins impact lipid pathways, and establish connections among fermentation-lipid biomarkers-protein. The recent leap of deep learning has been performed in food category recognition, agro-food freshness detection, and food flavor prediction and regulation. The proposed multimodal deep learning method includes four steps: (i) Forming data matrices based on data generated from different omics layers. (ii) Decomposing high-dimensional omics data according to self-attention mechanism. (iii) Constructing View Correlation Discovery Network to learn the cross-omics correlations and integrate different omics datasets. (iv) Depicting a biological network for lipid metabolism-centered quantitative multi-omics data analysis. Relying on the cytidine diphosphate-diacylglycerol synthase-mediated lipid metabolism regulates the glycerophospholipid composition of fermented dairy effectively. Innovative processing strategies including ohmic heating and pulsed electric field improve the sensory qualities and nutritional characteristics of the products.