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Machine learning (ML) tools are crucial for analyzing complex human microbiome data. This review compiles and classifies ML software to aid researchers in understanding microbial patterns and developing predictive health models.

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Area of Science:

  • Microbiology
  • Bioinformatics
  • Computational Biology

Background:

  • The human microbiome's impact on health is significant, but its complex data is challenging to analyze.
  • Machine learning (ML) offers powerful methods for pattern discovery in high-dimensional microbiome data.
  • Numerous ML-based software tools have been developed for microbiome data analysis.

Purpose of the Study:

  • To review the state-of-the-art ML tools for human microbiome data analysis.
  • To compile, catalog, and classify available ML software and frameworks.
  • To support researchers in selecting and utilizing appropriate ML resources for microbiome studies.

Main Methods:

  • Scoping review of ML-based software and framework resources for human microbiome data.
  • Organization of software by analysis type and ML techniques implemented.
  • Inclusion of usage examples, pitfalls, and limitations for each tool.

Main Results:

  • An extensive compilation of ML tools for microbiome analysis is presented.
  • Software resources are categorized, detailing algorithms and applications.
  • Insights into current limitations and considerations for ML tool development and usage are provided.

Conclusions:

  • This review offers valuable guidance for researchers using ML in human microbiome studies.
  • Standardization and benchmarking of ML tools are needed for reliable microbiome data analysis.
  • The compilation facilitates deeper exploration of specialized ML resources for microbiome research.