Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| PPI tīkla topoloģija× | HMMER profila meklēšana× | |
|---|---|---|
| Nozare | Bioinformātika | Bioinformātika |
| Saime | Process / pipeline | Process / pipeline |
| Izcelsmes gads≠ | 2000 | 1994 |
| Autors≠ | Peter Uetz | Sean Eddy |
| Tips≠ | Network analysis pipeline | Probabilistic sequence search pipeline |
| Pirmavots≠ | Uetz, P., Giot, L., Cagney, G., Mansfield, T. A., Judson, R. S., Knight, J. R., ... & Lomax, J. (2000). A comprehensive analysis of protein-protein interactions in Saccharomyces cerevisiae. Nature, 403(6770), 623-627. DOI ↗ | Krogh, A., Brown, M., Mian, I. S., Sjölander, K., & Haussler, D. (1994). Hidden Markov models in computational biology: applications to protein modeling. Journal of Molecular Biology, 235(5), 1501-1531. DOI ↗ |
| Citi nosaukumi | protein interaction networks, interactome analysis, network topology | profile-hidden Markov model, HMM profile search, HMMER |
| Saistītās | 3 | 3 |
| Kopsavilkums≠ | Protein-protein interaction network analysis identifies and characterizes the structural properties of cellular interaction networks. Pioneered by Uetz and colleagues through large-scale yeast two-hybrid screening, this approach reveals topological features like hubs, modules, and motifs that encode functional organization and disease associations. | HMMER profile search identifies distant protein sequence homologs using probabilistic models of protein families, known as profile Hidden Markov Models (HMMs). Developed by Eddy and colleagues, this method captures sequence variation patterns within protein families and detects homologs with far greater sensitivity than position-weight matrices or pairwise alignment. |
| ScholarGateDatu kopa ↗ |
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