Methoden vergleichen

	Maschinelles Lernen-gestützte Gen-Set-Anreicherungsanalyse	RNA-seq Differential Expression
Fachgebiet	Bioinformatik	Bioinformatik
Familie	Process / pipeline	Process / pipeline
Entstehungsjahr	2005 (GSEA); ML integration from ~2015 onward	2008–2010 (RNA-seq DE methodology established)
Urheber	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
Wegweisende Quelle	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.	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.
Aliasnamen	ML-GSEA, deep learning pathway enrichment, neural GSEA, ML-assisted pathway analysis	RNA-seq DE analysis, transcriptomic differential expression, bulk RNA-seq DE, DEA
Verwandt	6	6
Zusammenfassung	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.