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J. Dylan Spalding

dblp:84/4596 · DBLP profile ↗
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2ranked-venue papers
1as first author
0since 2021 · last 2020
0000-0002-4285-2493ORCID · verified

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1 · 1 first-authorApplied, interdisciplinary, general and emerging computing · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Interdisciplinary, comprehensive, and emerging computing
1 paper
Bioinformatics and computational biology · 100%

Topics — the 1 heaviest of 1, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Bioinformatics and computational biology
bioinformatics infrastructure
0.412020
The ELIXIR Core Data Resources: fundamental infrastructure for the life sciences · Bioinform. 2020
YearPublicationVenuePosition
2020 The ELIXIR Core Data Resources: fundamental infrastructure for the life sciences
abstract
SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Rachel Drysdale, Charles E. Cook, Robert Petryszak, Vivienne Baillie Gerritsen, Mary Barlow, Elisabeth Gasteiger, Franziska Gruhl, Jerry Lanfear, Rodrigo Lopez, Nicole Redaschi, Heinz Stockinger, Daniel Teixeira, Aravind Venkatesan, Alex Bateman, Alan J. Bridge, Guy Cochrane, Robert D. Finn, Frank Oliver Glöckner, Marc Hanauer, Thomas M. Keane, Luana Licata, Per Oksvold, Sandra E. Orchard, Christine A. Orengo, Helen E. Parkinson, Bengt Persson, Pablo Porras, Jordi Rambla De Argila, Ana Rath, Charlotte Rodwell, Ugis Sarkans, Dietmar Schomburg, Ian Sillitoe, J. Dylan Spalding, Mathias Uhlen, Sameer Velankar, Juan Antonio Vizcaíno, Kalle von Feilitzen, Christian von Mering, Andy Yates, Niklas Blomberg, Christine Durinx, Johanna R. McEntyre
Bioinform.36
2003 A genetic algorithm to sequence DNA using sequencing by hybridisation experimental data
abstract
Recent advancements in the biological sciences have created new problems for computational biology. One such problem is determining the sequence of DNA. One of the options for speeding up this process is to exploit the parallel nature of a 'sequencing by hybridisation' experiment to obtain the information from the physical structure of DNA. Various methods have been proposed to reconstruct DNA sequences from SBH data, including hybrid genetic algorithms. We describe a genetic algorithm which is more robust than previous methods, and gives results with a similar level of accuracy to alternative methods. The performance of the genetic algorithm is compared to alternative hybrid genetic algorithms, and improvements in the time the algorithm takes to return a result are recorded. We conclude by arguing that robust genetic methods may offer techniques to further improve on current methods.
J. Dylan Spalding, Cara MacNish
IEEE Congress on Evolutionary Computation1