Comparar métodos

	Machine learning-assisted gene set enrichment analysis	Análise de Enriquecimento de Vias
Área	Bioinformática	Bioinformática
Família	Process / pipeline	Process / pipeline
Ano de origem	2005 (GSEA); ML integration from ~2015 onward	2003–2005
Autor original	Subramanian et al. (GSEA foundation, 2005); various ML extensions thereafter	Mootha et al. (2003); systematised by Subramanian et al. (2005)
Tipo	Computational enrichment analysis with machine learning	Statistical functional annotation method
Fonte seminal	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.	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.
Outros nomes	ML-GSEA, deep learning pathway enrichment, neural GSEA, ML-assisted pathway analysis	PEA, overrepresentation analysis, ORA, functional enrichment analysis
Relacionados	6	6
Resumo	Machine learning-assisted gene set enrichment analysis (ML-GSEA) extends the classical GSEA framework by incorporating supervised or unsupervised ML models — such as random forests, neural networks, or deep learning architectures — to improve the detection, ranking, and biological interpretation of enriched gene sets from high-throughput expression data. The approach is particularly valuable for complex, non-linear gene-set relationships that classical enrichment statistics may miss.	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.