Methoden vergelijken
Bekijk de geselecteerde methoden naast elkaar; rijen die verschillen zijn gemarkeerd.
| Machine Learning-Assisted eQTL-Analyse× | Pathway-verrijkingsanalyse× | |
|---|---|---|
| Vakgebied | Bio-informatica | Bio-informatica |
| Familie | Process / pipeline | Process / pipeline |
| Jaar van ontstaan≠ | 2015 (key ML-eQTL methods; foundational eQTL work: Jansen & Nap 2001) | 2003–2005 |
| Grondlegger≠ | Gamazon et al. (PrediXcan), Zhou & Troyanskaya (DeepSEA); broader field ca. 2015-onward | Mootha et al. (2003); systematised by Subramanian et al. (2005) |
| Type≠ | Statistical-computational genomics pipeline | Statistical functional annotation method |
| Oorspronkelijke bron≠ | Gamazon, E. R., Wheeler, H. E., Shah, K. P., Mozaffari, S. V., Aquino-Michaels, K., Carroll, R. J., ... & Im, H. K. (2015). A gene-based association method for mapping traits using reference transcriptome data. Nature Genetics, 47(9), 1091-1098. 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 ↗ |
| Aliassen | ML-assisted eQTL analysis, ML eQTL mapping, deep learning eQTL, predictive eQTL modeling | PEA, overrepresentation analysis, ORA, functional enrichment analysis |
| Verwant | 6 | 6 |
| Samenvatting≠ | Machine learning-assisted eQTL analysis integrates supervised learning models — ranging from elastic-net regression to deep neural networks — into the classical eQTL framework to predict and map genetic variants that regulate gene expression. By training predictive models on reference panels (e.g., GTEx), the approach enables imputation of gene expression in cohorts lacking RNA data, substantially increasing statistical power and enabling trans-tissue generalisation. | Pathway enrichment analysis (PEA) is a statistical approach that takes a list of genes or proteins of interest — typically derived from a differential expression or proteomics experiment — and identifies which pre-defined biological pathways or functional gene sets are represented more often than expected by chance. By mapping individual molecular changes onto curated pathway knowledge bases such as KEGG, Gene Ontology, or Reactome, PEA translates long gene lists into interpretable biological processes, making it a central tool in the post-analysis of high-throughput omics experiments. |
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