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diceR: an R package for class discovery using an ensemble driven approach

Overview of attention for article published in BMC Bioinformatics, January 2018
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About this Attention Score

  • Good Attention Score compared to outputs of the same age (70th percentile)
  • Good Attention Score compared to outputs of the same age and source (67th percentile)

Mentioned by

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7 X users

Citations

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34 Dimensions

Readers on

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65 Mendeley
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1 CiteULike
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Title
diceR: an R package for class discovery using an ensemble driven approach
Published in
BMC Bioinformatics, January 2018
DOI 10.1186/s12859-017-1996-y
Pubmed ID
Authors

Derek S. Chiu, Aline Talhouk

Abstract

Given a set of features, researchers are often interested in partitioning objects into homogeneous clusters. In health research, cancer research in particular, high-throughput data is collected with the aim of segmenting patients into sub-populations to aid in disease diagnosis, prognosis or response to therapy. Cluster analysis, a class of unsupervised learning techniques, is often used for class discovery. Cluster analysis suffers from some limitations, including the need to select up-front the algorithm to be used as well as the number of clusters to generate, in addition, there may exist several groupings consistent with the data, making it very difficult to validate a final solution. Ensemble clustering is a technique used to mitigate these limitations and facilitate the generalization and reproducibility of findings in new cohorts of patients. We introduce diceR (diverse cluster ensemble in R), a software package available on CRAN: https://CRAN.R-project.org/package=diceR CONCLUSIONS: diceR is designed to provide a set of tools to guide researchers through a general cluster analysis process that relies on minimizing subjective decision-making. Although developed in a biological context, the tools in diceR are data-agnostic and thus can be applied in different contexts.

X Demographics

X Demographics

The data shown below were collected from the profiles of 7 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 65 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 65 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 15 23%
Student > Ph. D. Student 13 20%
Student > Bachelor 5 8%
Student > Master 3 5%
Student > Doctoral Student 2 3%
Other 8 12%
Unknown 19 29%
Readers by discipline Count As %
Agricultural and Biological Sciences 12 18%
Computer Science 5 8%
Mathematics 4 6%
Medicine and Dentistry 4 6%
Environmental Science 4 6%
Other 12 18%
Unknown 24 37%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 5. 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 16 January 2018.
All research outputs
#6,406,258
of 23,016,919 outputs
Outputs from BMC Bioinformatics
#2,463
of 7,316 outputs
Outputs of similar age
#141,178
of 473,640 outputs
Outputs of similar age from BMC Bioinformatics
#40
of 126 outputs
Altmetric has tracked 23,016,919 research outputs across all sources so far. This one has received more attention than most of these and is in the 72nd percentile.
So far Altmetric has tracked 7,316 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one has gotten more attention than average, scoring higher than 66% 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 473,640 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 70% of its contemporaries.
We're also able to compare this research output to 126 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 67% of its contemporaries.