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A comparison of random forests, boosting and support vector machines for genomic selection

Overview of attention for article published in BMC Proceedings, May 2011
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Among the highest-scoring outputs from this source (#38 of 294)
  • Good Attention Score compared to outputs of the same age (79th percentile)
  • High Attention Score compared to outputs of the same age and source (93rd percentile)

Mentioned by

twitter
4 tweeters
q&a
1 Q&A thread

Citations

dimensions_citation
152 Dimensions

Readers on

mendeley
224 Mendeley
citeulike
2 CiteULike
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Title
A comparison of random forests, boosting and support vector machines for genomic selection
Published in
BMC Proceedings, May 2011
DOI 10.1186/1753-6561-5-s3-s11
Pubmed ID
Authors

Joseph O Ogutu, Hans-Peter Piepho, Torben Schulz-Streeck

Abstract

Genomic selection (GS) involves estimating breeding values using molecular markers spanning the entire genome. Accurate prediction of genomic breeding values (GEBVs) presents a central challenge to contemporary plant and animal breeders. The existence of a wide array of marker-based approaches for predicting breeding values makes it essential to evaluate and compare their relative predictive performances to identify approaches able to accurately predict breeding values. We evaluated the predictive accuracy of random forests (RF), stochastic gradient boosting (boosting) and support vector machines (SVMs) for predicting genomic breeding values using dense SNP markers and explored the utility of RF for ranking the predictive importance of markers for pre-screening markers or discovering chromosomal locations of QTLs.

Twitter Demographics

The data shown below were collected from the profiles of 4 tweeters 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 224 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
France 2 <1%
Spain 2 <1%
Brazil 1 <1%
Canada 1 <1%
United Kingdom 1 <1%
Unknown 217 97%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 61 27%
Student > Master 39 17%
Researcher 38 17%
Student > Doctoral Student 17 8%
Student > Bachelor 12 5%
Other 29 13%
Unknown 28 13%
Readers by discipline Count As %
Agricultural and Biological Sciences 78 35%
Computer Science 26 12%
Mathematics 15 7%
Engineering 13 6%
Biochemistry, Genetics and Molecular Biology 11 5%
Other 44 20%
Unknown 37 17%

Attention Score in Context

This research output has an Altmetric Attention Score of 6. 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 06 October 2017.
All research outputs
#3,023,753
of 13,272,350 outputs
Outputs from BMC Proceedings
#38
of 294 outputs
Outputs of similar age
#50,020
of 248,871 outputs
Outputs of similar age from BMC Proceedings
#1
of 16 outputs
Altmetric has tracked 13,272,350 research outputs across all sources so far. Compared to these this one has done well and is in the 77th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 294 research outputs from this source. They receive a mean Attention Score of 2.8. This one has done well, scoring higher than 87% of its peers.
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 248,871 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 79% of its contemporaries.
We're also able to compare this research output to 16 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 93% of its contemporaries.