The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
In silico prediction of protein-protein interactions in human macrophages
Open accessAbstract
BACKGROUND: Protein-protein interaction (PPI) network analyses are highly valuable in deciphering and understanding the intricate organisation of cellular functions. Nevertheless, the majority of available protein-protein interaction networks are context-less, i.e. without any reference to the spatial, temporal or physiological conditions in which the interactions may occur. In this work, we are proposing a protocol to infer the most likely protein-protein interaction (PPI) network in human macrophages. RESULTS: We integrated the PPI dataset from the Agile Protein Interaction DataAnalyzer (APID) with different meta-data to infer a contextualized macrophage-specific interactome using a combination of statistical methods. The obtained interactome is enriched in experimentally verified interactions and in proteins involved in macrophage-related biological processes (i.e. immune response activation, regulation of apoptosis). As a case study, we used the contextualized interactome to highlight the cellular processes induced upon Mycobacterium tuberculosis infection. CONCLUSION: Our work confirms that contextualizing interactomes improves the biological significance of bioinformatic analyses. More specifically, studying such inferred network rather than focusing at the gene expression level only, is informative on the processes involved in the host response. Indeed, important immune features such as apoptosis are solely highlighted when the spotlight is on the protein interaction level.
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Cluster analysis and display of genome-wide expression patterns
Eisen, M. B., Spellman, P. T., Brown, P. O., & Botstein, D. (1998) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.95.25.14863 · Cited by 16,184
Kastan, M. B., Zhan, Q., El-Deiry, W. S., Carrier, F., Jacks, T., Walsh, W. V., Plunkett, B. S., Vogelstein, B., & Fornace, A. J. (1992) · Cell · doi:10.1016/0092-8674(92)90593-2 · Cited by 3,047
Global signatures of protein and mRNA expression levels
de Sousa Abreu, R., Penalva, L. O., Marcotte, E. M., & Vogel, C. (2009) · Molecular BioSystems · doi:10.1039/b908315d · Cited by 1,248
Sugasawa, K., Ng, J. M. Y., Masutani, C., Iwai, S., van der Spek, P. J., Eker, A. P. M., Hanaoka, F., Bootsma, D., & Hoeijmakers, J. H. J. (1998) · Molecular Cell · doi:10.1016/s1097-2765(00)80132-x · Cited by 899
Hwang, B. J., Ford, J. M., Hanawalt, P. C., & Chu, G. (1999) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.96.2.424 · Cited by 581
Cyclin G is a transcriptional target of the p53 tumor suppressor protein.
Okamoto, K., & Beach, D. (1994) · The EMBO Journal · doi:10.1002/j.1460-2075.1994.tb06807.x · Cited by 455
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Zou, H., & Hastie, T. (2005) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.1467-9868.2005.00503.x · Cited by 20,957
Metagenomic biomarker discovery and explanation
Segata, N., Izard, J., Waldron, L., Gevers, D., Miropolsky, L., Garrett, W. S., & Huttenhower, C. (2011) · Genome Biology · doi:10.1186/gb-2011-12-6-r60 · Cited by 16,192
Smyth, G. K. (2004) · Statistical Applications in Genetics and Molecular Biology · doi:10.2202/1544-6115.1027 · Cited by 11,829
Mootha, V. K., Lindgren, C. M., Eriksson, K.-F., Subramanian, A., Sihag, S., Lehar, J., Puigserver, P., Carlsson, E., Ridderstråle, M., Laurila, E., et al. (2003) · Nature Genetics · doi:10.1038/ng1180 · Cited by 10,197
Inferring tumour purity and stromal and immune cell admixture from expression data
Yoshihara, K., Shahmoradgoli, M., Martínez, E., Vegesna, R., Kim, H., Torres-Garcia, W., Treviño, V., Shen, H., Laird, P. W., Levine, D. A., et al. (2013) · Nature Communications · doi:10.1038/ncomms3612 · Cited by 9,778
Metadata
- Authors
- Souiai, O., Guerfali, F., Ben Miled, S., Brun, C., & Benkahla, A.
- Venue
- BMC Research Notes
- Published
- 2014
- Volume
- 7
- Issue
- 1
- Pages
- 157
- ISSN
- 1756-0500 (ISSN-L)
- OpenAlex
- W2157795344
- Cited by
- 10,803
- References
- 6
- FWCI
- 221.46
- Type
- journal article
- Language
- en
- Open access
- gold
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