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Recombination spot identification Based on gapped k-mers

Overview of attention for article published in Scientific Reports, March 2016
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (95th percentile)
  • High Attention Score compared to outputs of the same age and source (92nd percentile)

Mentioned by

news
1 news outlet
blogs
4 blogs
twitter
24 tweeters
wikipedia
3 Wikipedia pages

Citations

dimensions_citation
27 Dimensions

Readers on

mendeley
39 Mendeley
Title
Recombination spot identification Based on gapped k-mers
Published in
Scientific Reports, March 2016
DOI 10.1038/srep23934
Pubmed ID
Authors

Rong Wang, Yong Xu, Bin Liu

Abstract

Recombination is crucial for biological evolution, which provides many new combinations of genetic diversity. Accurate identification of recombination spots is useful for DNA function study. To improve the prediction accuracy, researchers have proposed several computational methods for recombination spot identification. The k-mer feature is one of the most useful features for modeling the properties and function of DNA sequences. However, it suffers from the inherent limitation. If the value of word length k is large, the occurrences of k-mers are closed to a binary variable, with a few k-mers present once and most k-mers are absent. This usually causes the sparse problem and reduces the classification accuracy. To solve this problem, we add gaps into k-mer and introduce a new feature called gapped k-mer (GKM) for identification of recombination spots. By using this feature, we present a new predictor called SVM-GKM, which combines the gapped k-mers and Support Vector Machine (SVM) for recombination spot identification. Experimental results on a widely used benchmark dataset show that SVM-GKM outperforms other highly related predictors. Therefore, SVM-GKM would be a powerful predictor for computational genomics.

Twitter Demographics

The data shown below were collected from the profiles of 24 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 39 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 39 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 11 28%
Researcher 7 18%
Student > Bachelor 4 10%
Professor > Associate Professor 3 8%
Student > Master 3 8%
Other 7 18%
Unknown 4 10%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 9 23%
Computer Science 8 21%
Agricultural and Biological Sciences 6 15%
Medicine and Dentistry 3 8%
Engineering 2 5%
Other 6 15%
Unknown 5 13%

Attention Score in Context

This research output has an Altmetric Attention Score of 48. 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 23 June 2021.
All research outputs
#580,275
of 18,925,022 outputs
Outputs from Scientific Reports
#6,575
of 101,635 outputs
Outputs of similar age
#13,049
of 273,393 outputs
Outputs of similar age from Scientific Reports
#233
of 2,941 outputs
Altmetric has tracked 18,925,022 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 96th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 101,635 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 17.0. This one has done particularly well, scoring higher than 93% 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 273,393 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 95% of its contemporaries.
We're also able to compare this research output to 2,941 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 92% of its contemporaries.