Linganisha mbinu
Pitia mbinu ulizochagua bega kwa bega; safu zinazotofautiana zinaangaziwa.
| Utafiti wa Ulinganifu wa Epigenome kwa Msingi wa Mtandao (Network EWAS)× | GWAS yenye msingi wa mtandao× | |
|---|---|---|
| Nyanja | Bioinformatiki | Bioinformatiki |
| Familia | Process / pipeline | Process / pipeline |
| Mwaka wa asili≠ | 2010s, consolidating 2012–2018 | 2011–2013 (early tools); mature framework by 2015 |
| Mwanzilishi≠ | Adapted from EWAS (Rakyan et al., 2011) and network-based genomic methods (e.g., Ideker & Sharan, 2008) | Jia et al. (dmGWAS, 2011); Baranzini et al.; multiple concurrent groups |
| Aina≠ | Integrative epigenomic analysis | Network-augmented association analysis |
| Chanzo asilia≠ | Rakyan, V. K., Down, T. A., Balding, D. J., & Beck, S. (2011). Epigenome-wide association studies for common human diseases. Nature Reviews Genetics, 12(8), 529–541. link ↗ | Wang, Q., Yu, H., Zhao, Z., & Jia, P. (2015). EW_dmGWAS: edge-weighted dense module search for genome-wide association studies and gene expression profiles. Bioinformatics, 31(15), 2591–2594. link ↗ |
| Majina mbadala | network EWAS, network-integrated EWAS, graph-based EWAS, network-based DNA methylation analysis | network GWAS, gene network GWAS, network-informed GWAS, NbGWAS |
| Zinazohusiana | 6 | 6 |
| Muhtasari≠ | Network-based EWAS extends conventional epigenome-wide association studies by overlaying differentially methylated positions or regions onto biological interaction networks — such as protein-protein interaction, co-expression, or gene regulatory networks — to identify functionally coherent epigenetic modules rather than isolated CpG hits. This integration increases statistical power for detecting weak signals and reveals coordinated epigenetic dysregulation across pathways. | Network-based GWAS integrates conventional genome-wide association study results with biological network data — such as protein-protein interaction (PPI) networks or gene co-expression graphs — to identify disease-relevant gene modules or subnetworks. Instead of reporting only the top individual SNPs, this approach propagates association signals through molecular interaction networks, surfacing gene clusters whose collective signal implicates them in complex-trait biology even when no single variant reaches genome-wide significance alone. |
| ScholarGateSeti ya data ↗ |
|
|