MiMeNet: Exploring microbiome-metabolome relationships using neural networks.

Area of Science:

• Microbiome research
• Metabolomics
• Computational biology

Background:

• Advances in omics studies reveal microbial roles in disease via metabolic interactions.
• Computational tools for analyzing these microbe-metabolite relationships are still developing.

Purpose of the Study:

• To introduce MiMeNet, a novel neural network framework designed for modeling microbe-metabolite relationships.
• To evaluate MiMeNet's performance against existing computational methods.

Main Methods:

• MiMeNet framework utilizing neural networks.
• Ten iterations of 10-fold cross-validation on three paired microbiome-metabolome datasets.
• Comparison with state-of-the-art linear models.

Main Results:

• MiMeNet significantly improved metabolite abundance prediction accuracy (mean Spearman correlation increase).
• MiMeNet identified a greater number of well-predicted metabolites compared to linear models.
• MiMeNet revealed network structures and functional groupings of microbes and metabolites.

Conclusions:

• MiMeNet is a powerful tool for analyzing microbe-metabolite interactions.
• The framework provides insights into metabolic dysregulation in diseases.
• MiMeNet facilitates hypothesis generation at the microbiome-metabolomics interface.