Genetic Improvement of Computational Biology Software

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@InProceedings{langdon:2017:ECCSB,
  author =       "William B. Langdon and Karina Zile",
  title =        "Genetic Improvement of Computational Biology
                 Software",
  booktitle =    "Evolutionary Computation in Computational Biology",
  year =         "2017",
  editor =       "Jose Santos and Julia Handl and Amarda Shehu and 
                 Mostafa Ellabaan",
  pages =        "1657--1660",
  address =      "Berlin",
  month =        "15-19 " # jul,
  keywords =     "genetic algorithms, genetic programming, genetic
                 improvement, GGGP, search based software engineering,
                 SBSE, software engineering, bioinformatics, next
                 generation sequencing, NGS, DNA sequences, microarray,
                 genechip, NCBI GEO, DNA sequences, GGGP, GI, GP, NGS,
                 big data cleanup, molecular biology, data cleansing, in
                 silico contamination, identification and correction of
                 mislabelled genes, big data cleanup, hitch-hiking
                 genes, 1k~geneomes, 1KGP, identification and correction
                 of mislabelled genes",
  isbn13 =       "978-1-4503-4939-0",
  URL =          "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/langdon_2017_ECCSB.pdf",
  URL =          "http://doi.acm.org/10.1145/3067695.3082540",
  DOI =          "doi:10.1145/3067695.3082540",
  acmid =        "3082540",
  size =         "4 pages",
  abstract =     "There is a cultural divide between computer scientists
                 and biologists that needs to be addressed. The two
                 disciplines used to be quite unrelated but many new
                 research areas have arisen from their synergy. We
                 selectively review two multi-disciplinary problems:
                 dealing with contamination in sequencing data
                 repositories and improving software using biology
                 inspired evolutionary computing. Through several
                 examples, we show that ideas from biology may result in
                 optimised code and provide surprising improvements that
                 overcome challenges in speed and quality trade-offs. On
                 the other hand, development of computational methods is
                 essential for maintaining contamination free databases.
                 Computer scientists and biologists must always be
                 sceptical of each others data, just as they would be of
                 their own.",
  notes =        "slides:
                 http://www.cs.ucl.ac.uk/staff/W.Langdon/gecco2017/langdon_2017_eccsb_slides.pdf

                 http://eccsb2017.irlab.org/ Also known as
                 \cite{Langdon:2017:GECCOa}
                 \cite{Langdon:2017:GIC:3067695.3082540} GECCO-2017 A
                 Recombination of the 26th International Conference on
                 Genetic Algorithms (ICGA-2017) and the 22nd Annual
                 Genetic Programming Conference (GP-2017)",
}

Genetic Programming entries for William B Langdon Karina Zile

Citations