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CancerSubtypes: an R/Bioconductor package for molecular cancer subtype identification, validation and visualization.

Overview of attention for article published in Bioinformatics, June 2017
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (87th percentile)
  • Good Attention Score compared to outputs of the same age and source (77th percentile)

Mentioned by

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1 news outlet
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13 X users

Citations

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187 Dimensions

Readers on

mendeley
89 Mendeley
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2 CiteULike
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Title
CancerSubtypes: an R/Bioconductor package for molecular cancer subtype identification, validation and visualization.
Published in
Bioinformatics, June 2017
DOI 10.1093/bioinformatics/btx378
Pubmed ID
Authors

Taosheng Xu, Thuc Duy Le, Lin Liu, Ning Su, Rujing Wang, Bingyu Sun, Antonio Colaprico, Gianluca Bontempi, Jiuyong Li

Abstract

Identifying molecular cancer subtypes from multi-omics data is an important step in the personalized medicine. We introduce CancerSubtypes , an R package for identifying cancer subtypes using multi-omics data, including gene expression, miRNA expression and DNA methylation data. CancerSubtypes integrates four main computational methods which are highly cited for cancer subtype identification and provides a standardized framework for data pre-processing, feature selection, and result follow-up analyses, including results computing, biology validation and visualization. The input and output of each step in the framework are packaged in the same data format, making it convenience to compare different methods. The package is useful for inferring cancer subtypes from an input genomic dataset, comparing the predictions from different well-known methods and testing new subtype discovery methods, as shown with different application scenarios in the Supplementary Material. The package is implemented in R and available under GPL-2 license from the Bioconductor website( http://bioconductor.org/packages/CancerSubtypes/ ). [email protected] , [email protected].

X Demographics

X Demographics

The data shown below were collected from the profiles of 13 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 89 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 89 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 16 18%
Student > Master 16 18%
Student > Ph. D. Student 15 17%
Other 7 8%
Student > Doctoral Student 6 7%
Other 10 11%
Unknown 19 21%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 19 21%
Agricultural and Biological Sciences 15 17%
Computer Science 15 17%
Medicine and Dentistry 6 7%
Engineering 3 3%
Other 8 9%
Unknown 23 26%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 16. 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 15 January 2022.
All research outputs
#1,983,845
of 23,342,092 outputs
Outputs from Bioinformatics
#1,007
of 8,020 outputs
Outputs of similar age
#40,117
of 318,303 outputs
Outputs of similar age from Bioinformatics
#17
of 71 outputs
Altmetric has tracked 23,342,092 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 91st percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 8,020 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 8.4. 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 318,303 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 87% of its contemporaries.
We're also able to compare this research output to 71 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 77% of its contemporaries.