GeNet: A Graph-Based Genetic Programming Framework for the Reverse Engineering of Gene Regulatory Networks

Created by W.Langdon from gp-bibliography.bib Revision:1.4524

  author =       "Leonardo Vanneschi and Matteo Mondini and 
                 Martino Bertoni and Alberto Ronchi and Mattia Stefano",
  title =        "{GeNet}: A Graph-Based Genetic Programming Framework
                 for the Reverse Engineering of Gene Regulatory
  booktitle =    "10th European Conference on Evolutionary Computation,
                 Machine Learning and Data Mining in Bioinformatics,
                 {EvoBIO 2012}",
  year =         "2012",
  month =        "11-13 " # apr,
  editor =       "Mario Giacobini and Leonardo Vanneschi and 
                 William S. Bush",
  series =       "LNCS",
  volume =       "7246",
  publisher =    "Springer Verlag",
  address =      "Malaga, Spain",
  pages =        "97--109",
  organisation = "EvoStar",
  isbn13 =       "978-3-642-29065-7",
  DOI =          "doi:10.1007/978-3-642-29066-4_9",
  keywords =     "genetic algorithms, genetic programming",
  abstract =     "A standard tree-based genetic programming system,
                 called GRNGen, for the reverse engineering of gene
                 regulatory networks starting from time series datasets,
                 was proposed in EvoBIO 2011. Despite the interesting
                 results obtained on the simple IRMA network, GRNGen has
                 some important limitations. For instance, in order to
                 reconstruct a network with GRNGen, one single
                 regression problem has to be solved by GP for each
                 gene. This entails a clear limitation on the size of
                 the networks that it can reconstruct, and this
                 limitation is crucial, given that real genetic networks
                 generally contain large numbers of genes. In this paper
                 we present a new system, called GeNet, which aims at
                 overcoming the main limitations of GRNGen, by directly
                 evolving entire networks using graph-based genetic
                 programming. We show that GeNet finds results that are
                 comparable, and in some cases even better, than GRNGen
                 on the small IRMA network, but, even more importantly
                 (and contrarily to GRNGen), it can be applied also to
                 larger networks. Last but not least, we show that the
                 time series datasets found in literature do not contain
                 a sufficient amount of information to describe the IRMA
                 network in detail.",
  notes =        "Part of \cite{Giacobini:2012:EvoBio} EvoBio'2012 held
                 in conjunction with EuroGP2012, EvoCOP2012,
                 EvoMusArt2012 and EvoApplications2012",

Genetic Programming entries for Leonardo Vanneschi Matteo Mondini Martino Bertoni Alberto Ronchi Mattia Stefano