The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Causal Protein-Signaling Networks Derived from Multiparameter Single-Cell Data
Abstract
Machine learning was applied for the automated derivation of causal influences in cellular signaling networks. This derivation relied on the simultaneous measurement of multiple phosphorylated protein and phospholipid components in thousands of individual primary human immune system cells. Perturbing these cells with molecular interventions drove the ordering of connections between pathway components, wherein Bayesian network computational methods automatically elucidated most of the traditionally reported signaling relationships and predicted novel interpathway network causalities, which we verified experimentally. Reconstruction of network models from physiologically relevant primary single cells might be applied to understanding native-state tissue signaling biology, complex drug actions, and dysfunctional signaling in diseased cells.
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Cites
Causality: Models, Reasoning, and Inference
Pearl, J. (2009) · doi:10.1017/cbo9780511803161 · Cited by 11,653
A N EW A PPROACH TO D ECODING L IFE : Systems Biology
Ideker, T., Galitski, T., & Hood, L. (2001) · Annual Review of Genomics and Human Genetics · doi:10.1146/annurev.genom.2.1.343 · Cited by 1,664
Seventeen-colour flow cytometry: unravelling the immune system
Perfetto, S. P., Chattopadhyay, P. K., & Roederer, M. (2004) · Nature Reviews Immunology · doi:10.1038/nri1416 · Cited by 1,019
Research works printed but not linked: 23
- J. Pearl Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference (Morgan Kaufmann San Mateo CA 1988). · DOI
- 10.1126/science.1094068 · DOI · Paper page
- 10.1089/106652700750050961 · DOI · Paper page
- K. Sachs, D. Gifford, T. Jaakkola, P. Sorger, D. A. Lauffenburger, Sci. STKE2002, pe38 (2002).
- 10.1093/bioinformatics/17.suppl_1.S215 · DOI · Paper page
- D. Pe'er, Sci. STKE2005, pl 4 (2005).
- A. J. Hartemink, D. K. Gifford, T. S. Jaakkola, R. A. Young, Pac. Symp. Biocomput.2001, 422 (2001).
- 10.1093/bioinformatics/bti056 · DOI
- 10.1093/clinchem/48.10.1819 · DOI · Paper page
- 10.1038/nbt0202-155 · DOI · Paper page
- O. D. Perez et al., Nat. Immunol.11, 1083 (2003).
- 10.1016/j.cell.2004.06.028 · DOI · Paper page
- D. Heckerman, in Learning in Graphical Models, M. I. Jordan, Ed. (MIT Press, Cambridge, MA, 1998), pp. 301–354.
- 10.1016/S0021-9258(17)42250-2 · DOI
- 10.1002/j.1460-2075.1995.tb07316.x · DOI · Paper page
- 10.1126/science.280.5360.109 · DOI · Paper page
- 10.1152/ajpcell.1998.274.1.C82 · DOI
- R. Fukuda, B. Kelly, G. L. Semenza, Cancer Res.63, 2330 (2003).
- 10.1186/1471-2105-3-34 · DOI · Paper page
- B. P. Kelley et al., Nucleic Acids Res.32, W83 (2004). · DOI · Paper page
- K. M. Nir Friedman S. Russell Proceedings of the Fourteenth Annual Conference on Uncertainty in Artificial Intelligence (Morgan Kaufmann San Francisco 1998).
- J. D. G. Irene, M. Ong, D. Page, Bioinformatics18, S241 (2002).
- 10.1016/j.clim.2003.11.015 · DOI
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Regev, A., Teichmann, S. A., Lander, E. S., Amit, I., Benoist, C., Birney, E., Bodenmiller, B., Campbell, P., Carninci, P., Clatworthy, M., et al. (2017) · eLife · doi:10.7554/elife.27041 · Cited by 2,232
Self-citationBendall, S. C., Simonds, E. F., Qiu, P., Amir, E.-A. D., Krutzik, P. O., Finck, R., Bruggner, R. V., Melamed, R., Trejo, A., Ornatsky, O. I., et al. (2011) · Science · doi:10.1126/science.1198704 · Cited by 2,279
Self-citationRecovering Gene Interactions from Single-Cell Data Using Data Diffusion
van Dijk, D., Sharma, R., Nainys, J., Yim, K., Kathail, P., Carr, A. J., Burdziak, C., Moon, K. R., Chaffer, C. L., Pattabiraman, D., et al. (2018) · Cell · doi:10.1016/j.cell.2018.05.061 · Cited by 1,829
Self-citationAmir, E.-A. D., Davis, K. L., Tadmor, M. D., Simonds, E. F., Levine, J. H., Bendall, S. C., Shenfeld, D. K., Krishnaswamy, S., Nolan, G. P., & Pe'er, D. (2013) · Nature Biotechnology · doi:10.1038/nbt.2594 · Cited by 1,550
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Metadata
- Authors
- Sachs, K., Perez, O., Pe'er, D., Lauffenburger, D. A., & Nolan, G. P.
- Venue
- Science
- Published
- 2005-04-22
- Volume
- 308
- Issue
- 5721
- Pages
- 523-529
- ISSN
- 0036-8075 (ISSN-L)
- 1095-9203
- OpenAlex
- W2073307618
- Cited by
- 1,708
- References
- 3
- FWCI
- 30.65
- Type
- journal article
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
