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| Мулти-омикс анализ за обогатяване на пътища× | Анализ на обогатяване на генни набори (GSEA)× | |
|---|---|---|
| Област | Биоинформатика | Биоинформатика |
| Семейство | Process / pipeline | Process / pipeline |
| Година на възникване≠ | 2014–2016 (multi-omics extension of enrichment methods established ~2005) | 2005 (seminal PNAS paper; predecessor concept in Mootha et al. 2003) |
| Създател≠ | Building on Subramanian et al. (2005); multi-omics integration formalised by Meng et al. and others (~2014–2016) | Aravind Subramanian, Pablo Tamayo, Vamsi K. Mootha, Jill P. Mesirov, Todd R. Golub, Eric S. Lander et al. (Broad Institute) |
| Тип≠ | Integrative pathway analysis pipeline | Functional genomics / enrichment analysis |
| Основополагащ източник≠ | Meng, C., Kuster, B., Culhane, A. C., & Gholami, A. M. (2014). A multivariate approach to the integration of multi-omics datasets. BMC Bioinformatics, 15, 162. link ↗ | 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. DOI ↗ |
| Други названия | multi-omics pathway analysis, integrated pathway enrichment, multi-layer pathway enrichment, MOPEA | GSEA, gene-set analysis, functional enrichment analysis, pathway-level enrichment |
| Свързани≠ | 1 | 5 |
| Резюме≠ | Multi-omics pathway enrichment analysis is a bioinformatics pipeline that integrates molecular data from two or more omics layers — such as transcriptomics, proteomics, metabolomics, and epigenomics — and tests whether the combined signal from those layers converges on specific biological pathways more than expected by chance. By considering multiple molecular levels simultaneously, it identifies pathway-level dysregulation that single-omics analyses would miss. | Gene Set Enrichment Analysis (GSEA) is a computational method that determines whether a predefined set of genes — representing a biological pathway, process, or function — shows statistically significant, coordinated differences between two biological conditions. Unlike simple fold-change filtering, GSEA operates on all measured genes ranked by a correlation metric, detecting subtle but consistent shifts across an entire pathway even when no single gene passes a significance threshold. |
| ScholarGateНабор от данни ↗ |
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