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Core Hunter II: fast core subset selection based on multiple genetic diversity measures using Mixed Replica search

Overview of attention for article published in BMC Bioinformatics, November 2012
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Title
Core Hunter II: fast core subset selection based on multiple genetic diversity measures using Mixed Replica search
Published in
BMC Bioinformatics, November 2012
DOI 10.1186/1471-2105-13-312
Pubmed ID
Authors

Herman De Beukelaer, Petr Smýkal, Guy F Davenport, Veerle Fack

Abstract

Sampling core subsets from genetic resources while maintaining as much as possible the genetic diversity of the original collection is an important but computationally complex task for gene bank managers. The Core Hunter computer program was developed as a tool to generate such subsets based on multiple genetic measures, including both distance measures and allelic diversity indices. At first we investigate the effect of minimum (instead of the default mean) distance measures on the performance of Core Hunter. Secondly, we try to gain more insight into the performance of the original Core Hunter search algorithm through comparison with several other heuristics working with several realistic datasets of varying size and allelic composition. Finally, we propose a new algorithm (Mixed Replica search) for Core Hunter II with the aim of improving the diversity of the constructed core sets and their corresponding generation times.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 44 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Mexico 2 5%
United States 1 2%
Netherlands 1 2%
Brazil 1 2%
Unknown 39 89%

Demographic breakdown

Readers by professional status Count As %
Researcher 16 36%
Student > Ph. D. Student 7 16%
Student > Bachelor 3 7%
Student > Master 3 7%
Student > Postgraduate 2 5%
Other 6 14%
Unknown 7 16%
Readers by discipline Count As %
Agricultural and Biological Sciences 30 68%
Biochemistry, Genetics and Molecular Biology 2 5%
Computer Science 2 5%
Economics, Econometrics and Finance 2 5%
Nursing and Health Professions 1 2%
Other 3 7%
Unknown 4 9%