Inferring Transcription Networks from Data

Created by W.Langdon from gp-bibliography.bib Revision:1.3949

@InCollection{Floares:2014:shbBNI,
  author =       "Alexandru G. Floares and Irina Luludachi",
  title =        "Inferring Transcription Networks from Data",
  booktitle =    "Springer Handbook of Bio-/Neuroinformatics",
  publisher =    "Springer",
  year =         "2014",
  editor =       "Nikola Kasabov",
  chapter =      "20",
  pages =        "311--326",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "978-3-642-30573-3",
  URL =          "http://www.springer.com/engineering/computational+intelligence+and+complexity/book/978-3-642-30573-3",
  DOI =          "doi:10.1007/978-3-642-30574-0_20",
  abstract =     "Reverse engineering of transcription networks is a
                 challenging bioinformatics problem. Ordinary
                 differential equation (ODEs) network models have their
                 roots in the physicochemical base of these networks,
                 but are difficult to build conventionally. Modelling
                 automation is needed and knowledge discovery in data
                 using computational intelligence methods is a solution.
                 The authors have developed a methodology for
                 automatically inferring ODE systems models from omics
                 data, based on genetic programming (GP), and illustrate
                 it on a real transcription network. The methodology
                 allows the network to be decomposed from the complex of
                 interacting cellular networks and to further decompose
                 each of its nodes, without destroying their
                 interactions. The structure of the network is not
                 imposed but discovered from data, and further
                 assumptions can be made about the parameters' values
                 and the mechanisms involved. The algorithms can deal
                 with unmeasured regulatory variables, like
                 transcription factors (TFs) and microRNA (miRNA or
                 miR). This is possible by introducing the regulome
                 probabilities concept and the techniques to compute
                 them. They are based on the statistical thermodynamics
                 of regulatory molecular interactions. Thus, the
                 resultant models are mechanistic and theoretically
                 founded, not merely data fittings. To our knowledge,
                 this is the first reverse engineering approach capable
                 of dealing with missing variables, and the accuracy of
                 all the models developed is greater than 99percent.",
  notes =        "GP RODES, GPTIPS

                 SAIA, OncoPredict Cancer Institute Cluj-Napoca, Str.
                 Republicii, Nr. 34-36, 400015, Cluj-Napoca, Romania",
}

Genetic Programming entries for Alexandru Floares Irina Luludachi

Citations