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      Causal protein-signaling networks derived from multiparameter single-cell data.

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          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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          Author and article information

          Journal
          Science
          Science (New York, N.Y.)
          American Association for the Advancement of Science (AAAS)
          1095-9203
          0036-8075
          Apr 22 2005
          : 308
          : 5721
          Affiliations
          [1 ] Biological Engineering Division, Massachusetts Institute of Technology (MIT), Cambridge, MA 02139, USA.
          Article
          308/5721/523
          10.1126/science.1105809
          15845847
          f8cc657b-569c-4c30-a7cf-caaaebdf9450
          History

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