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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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1 tweeter

Citations

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36 Dimensions

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34 Mendeley
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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.

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 34 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Mexico 2 6%
United States 1 3%
Netherlands 1 3%
Brazil 1 3%
Unknown 29 85%

Demographic breakdown

Readers by professional status Count As %
Researcher 15 44%
Student > Ph. D. Student 7 21%
Student > Master 3 9%
Student > Bachelor 2 6%
Professor 2 6%
Other 3 9%
Unknown 2 6%
Readers by discipline Count As %
Agricultural and Biological Sciences 28 82%
Biochemistry, Genetics and Molecular Biology 2 6%
Computer Science 1 3%
Economics, Econometrics and Finance 1 3%
Engineering 1 3%
Other 0 0%
Unknown 1 3%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 11 December 2012.
All research outputs
#7,762,556
of 12,373,386 outputs
Outputs from BMC Bioinformatics
#3,176
of 4,588 outputs
Outputs of similar age
#141,716
of 260,737 outputs
Outputs of similar age from BMC Bioinformatics
#256
of 378 outputs
Altmetric has tracked 12,373,386 research outputs across all sources so far. This one is in the 23rd percentile – i.e., 23% of other outputs scored the same or lower than it.
So far Altmetric has tracked 4,588 research outputs from this source. They receive a mean Attention Score of 4.9. This one is in the 21st percentile – i.e., 21% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 260,737 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 34th percentile – i.e., 34% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 378 others from the same source and published within six weeks on either side of this one. This one is in the 21st percentile – i.e., 21% of its contemporaries scored the same or lower than it.