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Stepwise classification of cancer samples using clinical and molecular data

Overview of attention for article published in BMC Bioinformatics, October 2011
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1 tweeter

Citations

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10 Dimensions

Readers on

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37 Mendeley
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1 CiteULike
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Title
Stepwise classification of cancer samples using clinical and molecular data
Published in
BMC Bioinformatics, October 2011
DOI 10.1186/1471-2105-12-422
Pubmed ID
Authors

Askar Obulkasim, Gerrit A Meijer, Mark A van de Wiel

Abstract

Combining clinical and molecular data types may potentially improve prediction accuracy of a classifier. However, currently there is a shortage of effective and efficient statistical and bioinformatic tools for true integrative data analysis. Existing integrative classifiers have two main disadvantages: First, coarse combination may lead to subtle contributions of one data type to be overshadowed by more obvious contributions of the other. Second, the need to measure both data types for all patients may be both unpractical and (cost) inefficient.

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 37 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 5 14%
Germany 1 3%
Canada 1 3%
Unknown 30 81%

Demographic breakdown

Readers by professional status Count As %
Researcher 14 38%
Student > Ph. D. Student 8 22%
Other 4 11%
Student > Doctoral Student 2 5%
Student > Master 2 5%
Other 3 8%
Unknown 4 11%
Readers by discipline Count As %
Agricultural and Biological Sciences 13 35%
Medicine and Dentistry 7 19%
Mathematics 3 8%
Biochemistry, Genetics and Molecular Biology 3 8%
Computer Science 3 8%
Other 4 11%
Unknown 4 11%

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 31 October 2011.
All research outputs
#7,762,553
of 12,373,386 outputs
Outputs from BMC Bioinformatics
#3,177
of 4,576 outputs
Outputs of similar age
#61,511
of 104,431 outputs
Outputs of similar age from BMC Bioinformatics
#21
of 26 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 4,576 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 104,431 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 25th percentile – i.e., 25% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 26 others from the same source and published within six weeks on either side of this one. This one is in the 11th percentile – i.e., 11% of its contemporaries scored the same or lower than it.