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	मशीन लर्निंग-सहायता प्राप्त माइक्रोबायोम विविधता विश्लेषण	Pathway Enrichment Analysis
क्षेत्र	जैव सूचना विज्ञान	जैव सूचना विज्ञान
परिवार	Process / pipeline	Process / pipeline
उद्भव वर्ष	2011–2016 (formalization of ML integration into microbiome pipelines)	2003–2005
प्रवर्तक	Pasolli, Segata and colleagues (meta-ML framework); broader field grew from Turnbaugh et al. human microbiome work	Mootha et al. (2003); systematised by Subramanian et al. (2005)
प्रकार	Computational pipeline (supervised/unsupervised ML + diversity metrics)	Statistical functional annotation method
मौलिक स्रोत	Pasolli, E., Truong, D. T., Malik, F., Waldron, L., & Segata, N. (2016). Machine Learning Meta-analysis of Large Metagenomic Datasets: Tools and Biological Insights. PLOS Computational Biology, 12(7), e1004977.	Subramanian, A., Tamayo, P., Mootha, V. K., Mukherjee, S., Ebert, B. L., Gillette, M. A., Paulovich, A., Pomeroy, S. L., Golub, T. R., Lander, E. S., & Mesirov, J. P. (2005). Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles. Proceedings of the National Academy of Sciences, 102(43), 15545–15550.
उपनाम	ML-based microbiome analysis, supervised microbiome diversity, microbiome ML classification, ML-driven alpha/beta diversity analysis	PEA, overrepresentation analysis, ORA, functional enrichment analysis
संबंधित	5	6
सारांश	Machine learning-assisted microbiome diversity analysis integrates classical alpha and beta diversity metrics with supervised or unsupervised ML models to classify host phenotypes, identify discriminant taxa, and uncover community-level signatures from 16S rRNA or shotgun metagenomic data. It extends traditional diversity analysis beyond descriptive statistics toward predictive and explanatory modelling across health, ecology, and environmental science.	Pathway enrichment analysis (PEA) is a statistical approach that takes a list of genes or proteins of interest — typically derived from a differential expression or proteomics experiment — and identifies which pre-defined biological pathways or functional gene sets are represented more often than expected by chance. By mapping individual molecular changes onto curated pathway knowledge bases such as KEGG, Gene Ontology, or Reactome, PEA translates long gene lists into interpretable biological processes, making it a central tool in the post-analysis of high-throughput omics experiments.