MMINP

MMINP predicts metabolic profiles associated with gut microbiota by modeling microbe-metabolite interactions, including relationships between metabolites and microbial genes, using the Two-Way Orthogonal Partial Least Squares (O2-PLS) algorithm.

Key Features:

• Predictive Framework: Predicts metabolic profiles that mediate relationships between gut microbiota and host health.
• O2-PLS Algorithm: Uses the Two-Way Orthogonal Partial Least Squares (O2-PLS) algorithm to consider both direct and indirect interactions and enhance prediction accuracy.
• Comparative Analysis: Demonstrates superior predictive value relative to existing methods such as MelonnPan and ENVIM.
• Feature Identification: Identifies factors influencing method performance, including training sample size, host disease state, and upstream data processing across technical platforms.

Scientific Applications:

• Diagnostic applications: Supports diagnostic studies by linking predicted metabolic profiles to host-microbiome states.
• Therapeutic applications: Informs targeted therapeutic interventions by revealing metabolites that influence microbial genes.
• Biomarker discovery: Enables identification of metabolic biomarkers for diseases.
• Host–microbiome interaction analysis: Provides insights into how metabolites influence microbial genes within the gut microbiome.

Methodology:

Integrates microbe-metabolite interaction data and analyzes them using the Two-Way Orthogonal Partial Least Squares (O2-PLS) algorithm, with predictive performance validated by comparison to MelonnPan and ENVIM.