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

By K. Sachs, O. Perez, Dana Pe'er, Douglas A. Lauffenburger, Garry P. Nolan. 2005

2,123 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Molecular Biology, Natural Language Processing

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Citations in the corpus

Bayesian Structure Learning with Generative Flow Networks (2022, methodology): “Table 2: Combining discrete interventional and observational ﬂow cytometry data (Sachs et al., 2005).”
Gradient-Based Neural DAG Learning (2019, background): “Structure learning and causal inference have many important applications in different areas of science such as genetics [5, 12], biology [13] and economics [7].”
Learning Neural Causal Models from Unknown Interventions (2019, methodology): “Causal structure learning using SCMs has been attempted in several disciplines including biology [2, 3], weather forecasting [4] and medicine [5, 6].”
Causal Generative Neural Networks (2017, methodology): “All algorithms were given the skeleton of the causal graph [Sachs et al. , 2005, Fig.”
DAGs with NO TEARS: Continuous Optimization for Structure Learning (2018, methodology): “At the same time, DAGs are popular models in practice, with applications in biology (Sachs et al., 2005), genetics (Zhang et al., 2013), machine learning (Koller and Friedman, 2009), and causal inference (Spirtes et al., 2000).”
Causal Discovery with Reinforcement Learning (2019, background): “Nevertheless, many real applications, like Sachs dataset (Sachs et al., 2005), have a relatively small number of variables.”
DAG-GNN: DAG Structure Learning with Graph Neural Networks (2019, methodology): “In Table 2, we compare DAG-GNN with DAG-NOTEARS as well as FSG, the fast greedy search method proposed by Ramsey et al. (2017), against the ground truth offered by Sachs et al. (2005).”
TOXICITY TESTING IN THE 21ST CENTURY: A VISION AND A STRATEGY (2010, background): “Predictive computational models derived from experimental studies have been developed to describe receptor-mediated cell communication and intracellular signal transduction (Sachs et al., 2005).”
Sequential Application of Anti-Cancer Drugs Enhances Cell Death by Re-wiring Apoptotic Signaling Networks (2012, background): “Predicting the efficacy of treatment and the optimal design of combination therapy will require a detailed understanding of how the DDR and other molecular signals are integrated and processed, how processing is altered by genetic perturbations commonly found in tumors, and how networks can be ‘‘rew”
CellProfiler: image analysis software for identifying and quantifying cell phenotypes (2006, background): “Because individual cells' responses are inhomogeneous [15], multiparametric single cell data from several types of instruments have proven much more powerful than whole-population data (for example, western blots or mRNA expression chips) for clustering genes, deriving causal networks, classifying p”
Bayesian inference of chemical kinetic models from proposed reactions (2015, methodology): “Note that the modeling of species interactions in our work contrasts with an approach commonly used in systems biology (Friedman and Koller, 2003; Sachs et al., 2002, 2005), where the relationships between species are described probabilistically using Bayesian networks.”
Structure and dynamics of molecular networks: A novel paradigm of drug discovery: A comprehensive review (2012, methodology): “As examples of this, the signaling network of T lymphocytes was reconstructed using single cell perturbations (Sachs et al., 2005), and the perturbations of 21 drug pairs were predicted from the reconstituted network of phospho-proteins and cell cycle markers of a human breast cancer cell line (Nela”
viSNE enables visualization of high dimensional single-cell data and reveals phenotypic heterogeneity of leukemia (2013, methodology): “Three healthy bone marrow samples (Marrow2–4) were assayed by mass cytometry using a panel of 31 phenotypic surface markers.”
Review of Causal Discovery Methods Based on Graphical Models (2019, methodology): “See Sachs et al. (2005) or Ramsey and Bryan (2018b) for the signiﬁcance of the variables. et al., 2012).”