Microbiomes harbor intricate associative connections, or co-occurrence, between member taxa that may be critical to the assembly and function of the microbial community. Identifying and characterizing these associations from metagenomic data may offer informative insight into disease progression and prevention. The organization of the microbiome is thought to be a singular assortative network, where interactions between taxa can readily be clustered into segregated and distinct communities. However, identifying distinct communities from microbiome data remains challenging. Microbiome community detector (MiCoDe) is a web tool that fits a weighted stochastic block model (WSBM) to a network edge-weight matrix to study the community structure of co-occurrence networks derived from metagenomic data.
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