Sammenlign metoder
Gennemgå dine valgte metoder side om side; rækker, der afviger, er fremhævet.
| Maskinlæringsassisteret "Pathway Enrichment"-analyse× | Gen-sætsanrigelsesanalyse (GSEA)× | |
|---|---|---|
| Fagområde | Bioinformatik | Bioinformatik |
| Familie | Process / pipeline | Process / pipeline |
| Oprindelsesår≠ | 2010s–present | 2005 (seminal PNAS paper; predecessor concept in Mootha et al. 2003) |
| Ophavsperson≠ | Multiple groups; early integration of ML with PEA circa 2010s (e.g., Ma'ayan Lab, Greene Lab) | Aravind Subramanian, Pablo Tamayo, Vamsi K. Mootha, Jill P. Mesirov, Todd R. Golub, Eric S. Lander et al. (Broad Institute) |
| Type≠ | Computational pipeline combining statistical enrichment with machine learning | Functional genomics / enrichment analysis |
| Oprindelig kilde≠ | Chen, E. Y., Tan, C. M., Kou, Y., Duan, Q., Wang, Z., Meirelles, G. V., Clark, N. R., & Ma'ayan, A. (2013). Enrichr: interactive and collaborative HTML5 gene list enrichment analysis tool. BMC Bioinformatics, 14, 128. 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 ↗ |
| Aliasser | ML-assisted PEA, ML-based pathway analysis, machine learning pathway enrichment, ML-enhanced gene set enrichment | GSEA, gene-set analysis, functional enrichment analysis, pathway-level enrichment |
| Relaterede≠ | 2 | 5 |
| Resumé≠ | Machine learning-assisted pathway enrichment analysis integrates classical statistical pathway enrichment methods — such as over-representation analysis or gene set enrichment analysis — with machine learning algorithms to improve sensitivity, handle high-dimensional omics data, and uncover non-linear biological patterns. The approach moves beyond ranking pathways by p-value alone, using ML models to weight gene contributions, distinguish signal from noise across many samples, and prioritize biologically meaningful pathways in complex datasets. | 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. |
| ScholarGateDatasæt ↗ |
|
|