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Publication Detail
Evolving better RNAfold structure prediction
  • Publication Type:
  • Authors:
    Langdon WB, Petke J, Lorenz R
  • Publisher:
    Springer International Publishing
  • Publication date:
  • Pagination:
    220, 236
  • Published proceedings:
    Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
  • Volume:
    10781 LNCS
  • ISBN-13:
  • Status:
  • Name of conference:
    EuroGP 2018, European Conference on Genetic Programming
  • Conference place:
    Parma, Italy
  • Conference start date:
  • Conference finish date:
  • Print ISSN:
© Springer International Publishing AG, part of Springer Nature 2018. Grow and graft genetic programming (GGGP) evolves more than 50000 parameters in a state-of-the-art C program to make functional source code changes which give more accurate predictions of how RNA molecules fold up. Genetic improvement updates 29% of the dynamic programming free energy model parameters. In most cases (50.3%) GI gives better results on 4655 known secondary structures from RNA_STRAND (29.0% are worse and 20.7% are unchanged). Indeed it also does better than parameters recommended by Andronescu, M., et al.: Bioinformatics 23(13) (2007) i19–i28.
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