- author = "Paola Vera-Licona and Abdul Jarrah and Luis David Garcia-Puente and John McGee and Reinhard Laubenbacher",
- title = "An algebra-based method for inferring gene regulatory networks",
- journal = "BMC Systems Biology",
- year = "2014",
- volume = "8",
- number = "37",
- month = mar,
- keywords = "genetic algorithms, genetic programming",
- ISSN = "1752-0509",
-
URL = "
https://doi.org/10.1186/1752-0509-8-37",
-
DOI = "
doi:10.1186/1752-0509-8-37",
- size = "16 pages",
- abstract = "The inference of gene regulatory networks (GRNs) from experimental observations is at the heart of systems biology. This includes the inference of both the network topology and its dynamics. While there are many algorithms available to infer the network topology from experimental data, less emphasis has been placed on methods that infer network dynamics. Furthermore, since the network inference problem is typically underdetermined, it is essential to have the option of incorporating into the inference process, prior knowledge about the network, along with an effective description of the search space of dynamic models. Finally, it is also important to have an understanding of how a given inference method is affected by experimental and other noise in the data used.",
-
notes = "EA applied to graph? Is it GP?
see also http://www.shsu.edu/~ldg005/data/recomb.pdf",
