Causal Protein-Signaling Networks Derived from Multiparameter Single-Cell Data Export

@article{citeulike:257225, 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. 10.1126/science.1105809}, author = {Sachs, Karen and Perez, Omar and Pe'er, Dana and Lauffenburger, Douglas A. and Nolan, Garry P.}, citeulike-article-id = {257225}, citeulike-linkout-0 = {http://dx.doi.org/10.1126/science.1105809}, citeulike-linkout-1 = {http://www.sciencemag.org/content/308/5721/523.abstract}, citeulike-linkout-2 = {http://www.sciencemag.org/content/308/5721/523.full.pdf}, citeulike-linkout-3 = {http://www.sciencemag.org/cgi/content/abstract/308/5721/523}, citeulike-linkout-4 = {http://view.ncbi.nlm.nih.gov/pubmed/15845847}, citeulike-linkout-5 = {http://www.hubmed.org/display.cgi?uids=15845847}, citeulike-linkout-6 = {http://www.sciencemag.org/cgi/content/full/308/5721/523}, day = {22}, doi = {10.1126/science.1105809}, issn = {1095-9203}, journal = {Science}, keywords = {biology, systems}, month = {April}, number = {5721}, pages = {523--529}, posted-at = {2006-05-07 02:35:27}, priority = {2}, title = {Causal Protein-Signaling Networks Derived from Multiparameter Single-Cell Data}, url = {http://dx.doi.org/10.1126/science.1105809}, volume = {308}, year = {2005} }

TY - JOUR ID - citeulike:257225 L3 - citeulike-article-id:257225 N2 - 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. 10.1126/science.1105809 IS - 5721 JF - Science SN - 1095-9203 EP - 529 TI - Causal Protein-Signaling Networks Derived from Multiparameter Single-Cell Data VL - 308 SP - 523 KW - biology KW - systems AU - Sachs, Karen AU - Perez, Omar AU - Pe'er, Dana AU - Lauffenburger, Douglas AU - Nolan, Garry PY - 2005/04/22/ UR - http://dx.doi.org/10.1126/science.1105809 ER -