The DOI matches its Crossref record; 30 of the 30 references it cites were found in a registry or an index.
Dynamic modeling of gene expression data
Open accessAbstract
We describe the time evolution of gene expression levels by using a time translational matrix to predict future expression levels of genes based on their expression levels at some initial time. We deduce the time translational matrix for previously published DNA microarray gene expression data sets by modeling them within a linear framework by using the characteristic modes obtained by singular value decomposition. The resulting time translation matrix provides a measure of the relationships among the modes and governs their time evolution. We show that a truncated matrix linking just a few modes is a good approximation of the full time translation matrix. This finding suggests that the number of essential connections among the genes is small.
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Cited by
Hierarchical Organization of Modularity in Metabolic Networks
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Modeling and Simulation of Genetic Regulatory Systems: A Literature Review
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Weak pairwise correlations imply strongly correlated network states in a neural population
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Gene regulatory network inference: Data integration in dynamic models—A review
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Reverse engineering gene networks using singular value decomposition and robust regression
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Metadata
- Authors
- Holter, N. S., Maritan, A., Cieplak, M., Fedoroff, N. V., & Banavar, J. R.
- Venue
- Proceedings of the National Academy of Sciences
- Published
- 2001-02-13
- Volume
- 98
- Issue
- 4
- Pages
- 1693-1698
- ISSN
- 0027-8424 (ISSN-L)
- 1091-6490
- OpenAlex
- W2113654344
- Cited by
- 300
- References
- 30
- FWCI
- 9.01
- Type
- journal article
- Language
- en
- Open access
- green
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- Holter, N. S., Maritan, A., Cieplak, M., Fedoroff, N. V., & Banavar, J. R. (2001). Dynamic modeling of gene expression data. Proceedings of the National Academy of Sciences. https://doi.org/10.1073/pnas.98.4.1693
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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.
