Causal Protein-Signaling Networks Derived from Multiparameter Single-Cell Data
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Summary
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.
- Type
- article
- Published
- 2005-04-22
- Cited by
- 2,145
- References
- 28
- OpenAlex
- https://openalex.org/W2073307618
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:8160280
Keywords
Computational biology, Signal transduction, Cell signaling, Systems biology, Biology
References
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- Inferring subnetworks from perturbed expression profiles
- The history and future of the fluorescence activated cell sorter and flow cytometry: a view from Stanford.
- Inferring Cellular Networks Using Probabilistic Graphical Models
- Bayesian analysis of signaling networks governing embryonic stem cell fate decisions
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- Causality : Models , Reasoning , and Inference
Cited by
- Phosphoproteomics: new insights into cellular signaling
- Linking data to models: data regression
- Isoelectric focusing technology quantifies protein signaling in 25 cells
- Nested effects models for high-dimensional phenotyping screens
- Bayesian methods for proteomics
- Achieving confidence in mechanism for drug discovery and development.
- Coevolutionary networks of splicing cis-regulatory elements
- Single cell cytometry of protein function in RNAi treated cells and in native populations
- Protein–protein interaction networks and subnetworks in the biology of disease
- Protein signaling networks from single cell fluctuations and information theory profiling.
- Comparing Statistical Methods for Constructing Large Scale Gene Networks
- Multiscale Models of Cell Signaling
- Minimum network constraint on reverse engineering to develop biological regulatory networks.
- Clonorchis sinensis lysophospholipase A upregulates IL-25 expression in macrophages as a potential pathway to liver fibrosis
- Predicting Causal Relationships from Biological Data: Applying Automated Causal Discovery on Mass Cytometry Data of Human Immune Cells
- Learning Network Structure of Financial Institutions from CDS Data
- Bayesian Network Approaches for Refining and Expanding Cellular and Immunological Pathways.
- Estimation and Inference in High Dimensional Networks, with Applications to Biological Systems.
- Mass Cytometry to Decipher the Mechanism of Nongenetic Drug Resistance in Cancer
- Analytical technologies for integrated single-cell analysis of human immune responses.
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