Analyzing Microbiome Data Using Machine Learning

Microbial communities are integral components of earths ecosystems. Investigating their temporal and spatial distribution patterns provides insights into the dynamics of the entire ecosystem. 

Analyzing amplicon- and metagenome-based microbiome data sets can uncover these patterns. However, these high-dimensional datasets are usually compositional, sparse, and characterized by non-linear relationships, complicating their analysis through conventional statistical methods. 

The application of various machine learning techniques, for example natural language processing algorithms, can circumvent these challenges. These methods facilitate the extraction of essential ecological information and the identification of interpretable patterns, thereby enhancing the understanding of microbial communities and the respective ecosystems.