Linganisha mbinu

	Uchambuzi wa Uboreshaji wa Seti za Jeni kwa Msaada wa Kujifunza kwa Mashine	Uchambuzi wa Utekelezaji Tofauti wa RNA-seq
Nyanja	Bioinformatiki	Bioinformatiki
Familia	Process / pipeline	Process / pipeline
Mwaka wa asili	2005 (GSEA); ML integration from ~2015 onward	2008–2010 (RNA-seq DE methodology established)
Mwanzilishi	Subramanian et al. (GSEA foundation, 2005); various ML extensions thereafter	Multiple groups; foundational methods from Anders & Huber (DESeq, 2010), Robinson, McCarthy & Smyth (edgeR, 2010)
Aina	Computational enrichment analysis with machine learning	Quantitative genomics pipeline
Chanzo asilia	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.
Majina mbadala	ML-GSEA, deep learning pathway enrichment, neural GSEA, ML-assisted pathway analysis	RNA-seq DE analysis, transcriptomic differential expression, bulk RNA-seq DE, DEA
Zinazohusiana	6	6
Muhtasari	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.