The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Using the principle of entropy maximization to infer genetic interaction networks from gene expression patterns
Open accessAbstract
We describe a method based on the principle of entropy maximization to identify the gene interaction network with the highest probability of giving rise to experimentally observed transcript profiles. In its simplest form, the method yields the pairwise gene interaction network, but it can also be extended to deduce higher-order interactions. Analysis of microarray data from genes in Saccharomyces cerevisiae chemostat cultures exhibiting energy metabolic oscillations identifies a gene interaction network that reflects the intracellular communication pathways that adjust cellular metabolic activity and cell division to the limiting nutrient conditions that trigger metabolic oscillations. The success of the present approach in extracting meaningful genetic connections suggests that the maximum entropy principle is a useful concept for understanding living systems, as it is for other complex, nonequilibrium systems.
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Cites
Information theory and statistical mechanics
Jaynes, E. T. (1957) · Physical Review · doi:10.1103/physrev.106.620 · Cited by 12,931
Information Theory and Statistical Mechanics. II
Jaynes, E. T. (1957) · Physical Review · doi:10.1103/physrev.108.171 · Cited by 3,253
Inferring Genetic Networks and Identifying Compound Mode of Action via Expression Profiling
Gardner, T. S., di Bernardo, D., Lorenz, D., & Collins, J. J. (2003) · Science · doi:10.1126/science.1081900 · Cited by 1,117
Nicotinamide and PNC1 govern lifespan extension by calorie restriction in Saccharomyces cerevisiae
Anderson, R. M., Bitterman, K. J., Wood, J. G., Medvedik, O., & Sinclair, D. A. (2003) · Nature · doi:10.1038/nature01578 · Cited by 736
Cited by
Protein 3D Structure Computed from Evolutionary Sequence Variation
Marks, D. S., Colwell, L. J., Sheridan, R., Hopf, T. A., Pagnani, A., Zecchina, R., & Sander, C. (2011) · PLoS ONE · doi:10.1371/journal.pone.0028766 · Cited by 1,144
High-order social interactions in groups of mice
Shemesh, Y., Sztainberg, Y., Forkosh, O., Shlapobersky, T., Chen, A., & Schneidman, E. (2013) · eLife · doi:10.7554/elife.00759 · Cited by 213
A Functional Taxonomy of Music Generation Systems
Herremans, D., Chuan, C.-H., & Chew, E. (2018) · ACM Computing Surveys · doi:10.1145/3108242 · Cited by 160
Reverse engineering and analysis of large genome-scale gene networks
Aluru, M., Zola, J., Nettleton, D., & Aluru, S. (2013) · Nucleic Acids Research · doi:10.1093/nar/gks904 · Cited by 34
Contact Potential for Structure Prediction of Proteins and Protein Complexes from Potts Model
Anishchenko, I., Kundrotas, P. J., & Vakser, I. A. (2018) · Biophysical Journal · doi:10.1016/j.bpj.2018.07.035 · Cited by 29
Metadata
- Authors
- Lezon, T. R., Banavar, J. R., Cieplak, M., Maritan, A., & Fedoroff, N. V.
- Venue
- Proceedings of the National Academy of Sciences
- Published
- 2006-12-12
- Volume
- 103
- Issue
- 50
- Pages
- 19033-19038
- ISSN
- 0027-8424 (ISSN-L)
- 1091-6490
- OpenAlex
- W2006333907
- Cited by
- 269
- References
- 4
- FWCI
- 3.44
- Type
- journal article
- Language
- en
- Open access
- green
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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.
