Statistical Model for Biochemical Network Inference
Article Properties
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LanguageEnglish
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DOI (url)
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Publication Date2013/01/01
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Indian UGC (journal)
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Refrences32
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Citations5
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Gheorghe Craciun
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Jaejik Kim
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Casian Pantea
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Grzegorz A. Rempala
Cite
Craciun, Gheorghe, et al. “Statistical Model for Biochemical Network Inference”. Communications in Statistics - Simulation and Computation, vol. 42, no. 1, 2013, pp. 121-37, https://doi.org/10.1080/03610918.2011.633200.
Craciun, G., Kim, J., Pantea, C., & Rempala, G. A. (2013). Statistical Model for Biochemical Network Inference. Communications in Statistics - Simulation and Computation, 42(1), 121-137. https://doi.org/10.1080/03610918.2011.633200
Craciun, Gheorghe, Jaejik Kim, Casian Pantea, and Grzegorz A. Rempala. “Statistical Model for Biochemical Network Inference”. Communications in Statistics - Simulation and Computation 42, no. 1 (2013): 121-37. https://doi.org/10.1080/03610918.2011.633200.
Craciun G, Kim J, Pantea C, Rempala GA. Statistical Model for Biochemical Network Inference. Communications in Statistics - Simulation and Computation. 2013;42(1):121-37.
Journal Categories
Science
Mathematics
Science
Mathematics
Probabilities
Mathematical statistics
Refrences
| Title | Journal | Journal Categories | Citations | Publication Date |
|---|---|---|---|---|
| Title | 2004 | |||
| Title | 1968 | |||
| Title | Progress in Biophysics and Molecular Biology |
| 2004 | |
| 10.1007/978-1-4757-3803-2 | 2003 | |||
| 10.1007/978-1-4757-3803-2 | Bernoulli |
| 2002 |
Citations
| Title | Journal | Journal Categories | Citations | Publication Date |
|---|---|---|---|---|
Identifiability of stochastically modelled reaction networks | European Journal of Applied Mathematics |
| 2021 | |
| On classes of reaction networks and their associated polynomial dynamical systems | Journal of Mathematical Chemistry |
| 7 | 2020 |
Testing structural identifiability by a simple scaling method | PLOS Computational Biology |
| 16 | 2020 |
| Inferring reaction network structure from single-cell, multiplex data, using toric systems theory | PLOS Computational Biology |
| 9 | 2019 |
Inference of gene regulatory networks from genome-wide knockout fitness data | Bioinformatics |
| 9 | 2012 |
Citations Analysis
The category
Science: Chemistry: Organic chemistry: Biochemistry 3
is the most commonly referenced area in studies that cite this article. The first research to cite this article was titled Inference of gene regulatory networks from genome-wide knockout fitness data and was published in 2012. The most recent citation comes from a 2021 study titled Identifiability of stochastically modelled reaction networks. This article reached its peak citation in 2020, with 2 citations. It has been cited in 4 different journals, 50% of which are open access. Among related journals, the PLOS Computational Biology cited this research the most, with 2 citations. The chart below illustrates the annual citation trends for this article.