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| Анализ на диференциална експресия на РНК-сек с помощта на машинно обучение× | Анализ на обогатяване на генни набори (GSEA)× | |
|---|---|---|
| Област | Биоинформатика | Биоинформатика |
| Семейство | Process / pipeline | Process / pipeline |
| Година на възникване≠ | 2015–2019 (rapid development period) | 2005 (seminal PNAS paper; predecessor concept in Mootha et al. 2003) |
| Създател≠ | Multiple groups; scVI (Lopez et al., 2018) and DCA (Eraslan et al., 2019) are landmark tools | Aravind Subramanian, Pablo Tamayo, Vamsi K. Mootha, Jill P. Mesirov, Todd R. Golub, Eric S. Lander et al. (Broad Institute) |
| Тип≠ | Computational bioinformatics pipeline | Functional genomics / enrichment analysis |
| Основополагащ източник≠ | Lopez, R., Regier, J., Cole, M. B., Jordan, M. I., & Yosef, N. (2018). Deep generative modeling for single-cell transcriptomics. Nature Methods, 15(12), 1053–1058. 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 | ML-based DE analysis, deep learning RNA-seq DE, neural network differential expression, ML-augmented transcriptomics | GSEA, gene-set analysis, functional enrichment analysis, pathway-level enrichment |
| Свързани | 5 | 5 |
| Резюме≠ | Machine learning-assisted RNA-seq differential expression analysis augments classical statistical DE testing (DESeq2, edgeR, limma-voom) with ML models — including neural networks, random forests, and variational autoencoders — to better handle the high dimensionality, zero-inflation, and batch effects inherent in RNA-seq count data. The approach improves feature selection, noise reduction, and detection power, especially in large or complex experimental designs. | 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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