Resources for the benchmarking of KEGG [1] [2] [3], Reactome [4] [5], and WikiPathways [6] [7] [8] on statistical enrichment and predictive Modeling using PathwayForte.
If you find pathway_forte useful for your work, please consider citing [9]:
| [9] | Mubeen, S., Hoyt, C. T., Gemünd, A., Hofmann-Apitius, M., Fröhlich, H., & Domingo-Fernández, D. (2019). The Impact of Pathway Database Choice on Statistical Enrichment Analysis and Predictive Modeling. Front. Genet., 10:1203. |
- Notebooks. Jupyter notebooks showing the results of the analysis.
- R. Scripts to download, query and preprocessing TCGA datasets.
- Input: Directories containing gmt files, TCGA gene expression data, enrichment scores and test data used in the paper (in case proprocessing has been already conducted)
| [1] | Kanehisa, M., et al. (2017) KEGG: new perspectives on genomes, pathways, diseases and drugs. Nucleic Acids Res. 45, D353-D361. |
| [2] | Kanehisa, M., et al. (2016). KEGG as a reference resource for gene and protein annotation. Nucleic Acids Res. 44, D457-D462. |
| [3] | Kanehisa, M. and Goto, S. (2000). KEGG: Kyoto Encyclopedia of Genes and Genomes. Nucleic Acids Res. 28, 27-30. |
| [4] | Fabregat, A. et al. (2016). The Reactome Pathway Knowledgebase. Nucleic Acids Res 44. Database issue: D481–D487. |
| [5] | Croft, D. et al. (2014). The Reactome Pathway Knowledgebase. Nucleic Acids Res 42. Database issue: D472–D477. |
| [6] | Slenter, D. N., et al (2018). WikiPathways: a multifaceted pathway database bridging metabolomics to other omics research. Nucleic Acids Research, 46(D1), D661–D667. |
| [7] | Kutmon, M., et al. (2016). WikiPathways: capturing the full diversity of pathway knowledge. Nucleic Acids Res., 44, D488-D494. |
| [8] | Kelder, T., et al. (201). WikiPathways: building research communities on biological pathways. Nucleic Acids Res. Jan;40(Database issue):D1301-7 |