mmvec

mmvec estimates conditional probabilities between microorganisms and metabolites using a neural network to infer microbe–metabolite interactions from multiomics co-occurrence data.

Key Features:

• Neural Network Architecture: Employs a neural network that models the conditional probability of metabolites given the presence of specific microorganisms.
• Multiomics Integration: Integrates diverse omics datasets to analyze co-occurrence patterns across biological layers.
• Statistical Robustness: Addresses statistical challenges in cross-omics interaction inference to provide reliable interaction estimates.

Scientific Applications:

• Environmental Microbiome Studies: Applied to datasets such as desert soil biocrust wetting to elucidate microbe–metabolite relationships in natural ecosystems.
• Clinical Research: Used to analyze complex microbiomes such as cystic fibrosis lung environments to study disease-associated microbial interactions.
• Disease Association Studies: Facilitates discovery of links between microbially produced metabolites and diseases, exemplified by inflammatory bowel disease research.

Methodology:

Uses neural networks to compute conditional probabilities that a metabolite is present given a specific microorganism, inferring interactions from observed co-occurrence patterns within multiomics datasets.