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Cited by 429

The DOI matches its Crossref record; 11 of the 11 references it cites were found in a registry or an index.

BioNetGen: software for rule-based modeling of signal transduction based on the interactions of molecular domains

Open access
Blinov, Michael L., Faeder, James R., Goldstein, Byron, Hlavacek, William S.·2004·Bioinformatics

Abstract

Summary: BioNetGen allows a user to create a computational model that characterizes the dynamics of a signal transduction system, and that accounts comprehensively and precisely for specified enzymatic activities, potential post-translational modifications and interactions of the domains of signaling molecules. The output defines and parameterizes the network of molecular species that can arise during signaling and provides functions that relate model variables to experimental readouts of interest. Models that can be generated are relevant for rational drug discovery, analysis of proteomic data and mechanistic studies of signal transduction. Availability: http://cellsignaling.lanl.gov/bionetgen

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Crossref · 10.1093/bioinformatics/bth378

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Metadata

Authors
Blinov, M. L., Faeder, J. R., Goldstein, B., & Hlavacek, W. S.
Venue
Bioinformatics
Published
2004-06-24
Volume
20
Issue
17
Pages
3289-3291
ISSN
  • 1367-4803 (ISSN-L)
  • 1367-4811
OpenAlex
W2163493106
Cited by
429
References
11
FWCI
4.15
Type
journal article
Language
en
Open access
bronze

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CoChat. (2026). Citation check: BioNetGen: software for rule-based modeling of signal transduction based on the interactions of molecular domains. https://app.cochat.ai/papers/10.1093/bioinformatics/bth378
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Blinov, M. L., Faeder, J. R., Goldstein, B., & Hlavacek, W. S. (2004). BioNetGen: software for rule-based modeling of signal transduction based on the interactions of molecular domains. Bioinformatics. https://doi.org/10.1093/bioinformatics/bth378

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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.