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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
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
- 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
Cited by
Sparse inverse covariance estimation with the graphical lasso
Friedman, J., Hastie, T., & Tibshirani, R. (2008) · Biostatistics · doi:10.1093/biostatistics/kxm045 · Cited by 6,355
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
Self-citation
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Causal Protein-Signaling Networks Derived from Multiparameter Single-Cell Data. https://app.cochat.ai/papers/10.1126/science.1105809
- The paper
- Sachs, K., Perez, O., Pe'er, D., Lauffenburger, D. A., & Nolan, G. P. (2005). Causal Protein-Signaling Networks Derived from Multiparameter Single-Cell Data. Science. https://doi.org/10.1126/science.1105809
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
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.
