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| Verkkopohjainen kopiolukuvaihteluanalyysi× | Geenien joukon rikastumisanalyysi (GSEA)× | |
|---|---|---|
| Tieteenala | Bioinformatiikka | Bioinformatiikka |
| Menetelmäperhe | Process / pipeline | Process / pipeline |
| Syntyvuosi≠ | 2011–2015 | 2005 (seminal PNAS paper; predecessor concept in Mootha et al. 2003) |
| Kehittäjä≠ | Fabio Vandin, Benjamin Raphael and colleagues (HotNet framework); Matthew Leiserson et al. (HotNet2) | Aravind Subramanian, Pablo Tamayo, Vamsi K. Mootha, Jill P. Mesirov, Todd R. Golub, Eric S. Lander et al. (Broad Institute) |
| Tyyppi≠ | Computational network analysis pipeline | Functional genomics / enrichment analysis |
| Alkuperäislähde≠ | Vandin, F., Upfal, E., & Raphael, B. J. (2012). De novo discovery of mutated driver pathways in cancer. Genome Research, 22(2), 375–385. DOI ↗ | 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 ↗ |
| Rinnakkaisnimet | network CNV analysis, CNV network propagation, graph-based CNV analysis, network-integrated copy number analysis | GSEA, gene-set analysis, functional enrichment analysis, pathway-level enrichment |
| Liittyvät≠ | 6 | 5 |
| Tiivistelmä≠ | Network-based copy number variation analysis integrates genome-wide CNV data with biological interaction networks — such as protein-protein interaction (PPI) or pathway networks — to identify functionally coherent regions, driver genes, and altered subnetworks that raw CNV calling alone would miss. By propagating CNV signals through the network graph, the method reveals coordinated genomic dosage imbalances that converge on common biological functions, making it especially powerful in cancer genomics and rare-disease studies. | 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. |
| ScholarGateAineisto ↗ |
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