CMiNet

CMiNet constructs consensus microbiome networks by integrating multiple inference algorithms to produce robust network structures that reflect underlying biological interactions.

Key Features:

• Multi-Algorithm Integration: CMiNet integrates up to ten widely used inference algorithms for network construction.
• Method Diversity: CMiNet supports both correlation-based and conditional dependence-based methods.
• Consensus Filtering: CMiNet employs a consensus-based filtering strategy that retains only edges supported by multiple methods.
• Network Flexibility: CMiNet provides options to construct individual or consensus networks across different approaches.

Scientific Applications:

• Gut Microbiome Analysis: CMiNet was demonstrated using gut microbiome datasets for consensus network construction.
• Soil Microbiome and Disease Association: CMiNet enabled consistent identification of disease-associated taxa in soil microbiome datasets, including Ktedonobacteria, Acidobacteriae, Vicinamibacteria, MB-A2-108, Planctomycetes, and Anaerolineae.

Methodology:

CMiNet integrates multiple inference algorithms and applies consensus-based filtering to retain edges supported by multiple methods.