Jämför metoder
Granska de valda metoderna sida vid sida; rader som skiljer sig är markerade.
| Maskininlärningsassisterad genuppsättningsanrikningsanalys× | RNA-seq Differential Expression× | |
|---|---|---|
| Ämnesområde | Bioinformatik | Bioinformatik |
| Familj | Process / pipeline | Process / pipeline |
| Ursprungsår≠ | 2005 (GSEA); ML integration from ~2015 onward | 2008–2010 (RNA-seq DE methodology established) |
| Upphovsperson≠ | Subramanian et al. (GSEA foundation, 2005); various ML extensions thereafter | Multiple groups; foundational methods from Anders & Huber (DESeq, 2010), Robinson, McCarthy & Smyth (edgeR, 2010) |
| Typ≠ | Computational enrichment analysis with machine learning | Quantitative genomics pipeline |
| Ursprungskälla≠ | 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 ↗ | Love, M. I., Huber, W., & Anders, S. (2014). Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biology, 15(12), 550. DOI ↗ |
| Alias | ML-GSEA, deep learning pathway enrichment, neural GSEA, ML-assisted pathway analysis | RNA-seq DE analysis, transcriptomic differential expression, bulk RNA-seq DE, DEA |
| Närliggande | 6 | 6 |
| Sammanfattning≠ | 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. | RNA-seq differential expression (DE) analysis identifies genes whose transcript abundance differs significantly between two or more biological conditions — for example, treated versus control, or diseased versus healthy tissue. Starting from raw sequencing reads, the pipeline moves through alignment, count-based normalization, statistical modeling of count dispersion, hypothesis testing, and multiple-testing correction to produce a ranked list of differentially expressed genes accompanied by fold-change estimates and adjusted p-values. |
| ScholarGateDatamängd ↗ |
|
|