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Evolving better RNAfold structure prediction

Langdon, WB; Petke, J; Lorenz, R; (2018) Evolving better RNAfold structure prediction. In: Genetic Programming. EuroGP 2018. (pp. pp. 220-236). Springer International Publishing: Cham, Switzerland. Green open access

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Abstract

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.

Type: Proceedings paper
Title: Evolving better RNAfold structure prediction
Event: EuroGP 2018, European Conference on Genetic Programming, 4-6 April 2018, Parma, Italy
ISBN-13: 9783319775524
Open access status: An open access version is available from UCL Discovery
DOI: 10.1007/978-3-319-77553-1_14
Publisher version: https://doi.org/10.1007/978-3-319-77553-1_14
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: genetic improvement, genetic algorithms, genetic programming, software engineering, SBSE, software maintenance of empirical constants, Bioinformatics, local search, genomic and phenotypic Tabu restrictions, genetic repair
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
URI: https://discovery.ucl.ac.uk/id/eprint/10048826
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