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ENCAPP: elastic-net-based prognosis prediction and biomarker discovery for human cancers

Overview of attention for article published in BMC Genomics, April 2015
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (76th percentile)

Mentioned by

twitter
3 tweeters
patent
1 patent
facebook
1 Facebook page

Citations

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23 Dimensions

Readers on

mendeley
48 Mendeley
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Title
ENCAPP: elastic-net-based prognosis prediction and biomarker discovery for human cancers
Published in
BMC Genomics, April 2015
DOI 10.1186/s12864-015-1465-9
Pubmed ID
Authors

Jishnu Das, Kaitlyn M Gayvert, Florentina Bunea, Marten H Wegkamp, Haiyuan Yu

Abstract

With the explosion of genomic data over the last decade, there has been a tremendous amount of effort to understand the molecular basis of cancer using informatics approaches. However, this has proven to be extremely difficult primarily because of the varied etiology and vast genetic heterogeneity of different cancers and even within the same cancer. One particularly challenging problem is to predict prognostic outcome of the disease for different patients. Here, we present ENCAPP, an elastic-net-based approach that combines the reference human protein interactome network with gene expression data to accurately predict prognosis for different human cancers. Our method identifies functional modules that are differentially expressed between patients with good and bad prognosis and uses these to fit a regression model that can be used to predict prognosis for breast, colon, rectal, and ovarian cancers. Using this model, ENCAPP can also identify prognostic biomarkers with a high degree of confidence, which can be used to generate downstream mechanistic and therapeutic insights. ENCAPP is a robust method that can accurately predict prognostic outcome and identify biomarkers for different human cancers.

Twitter Demographics

The data shown below were collected from the profiles of 3 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 48 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 2%
India 1 2%
Belgium 1 2%
Unknown 45 94%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 17 35%
Researcher 11 23%
Student > Master 5 10%
Student > Bachelor 4 8%
Student > Doctoral Student 2 4%
Other 5 10%
Unknown 4 8%
Readers by discipline Count As %
Agricultural and Biological Sciences 11 23%
Biochemistry, Genetics and Molecular Biology 9 19%
Computer Science 9 19%
Medicine and Dentistry 7 15%
Immunology and Microbiology 2 4%
Other 4 8%
Unknown 6 13%

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 08 June 2017.
All research outputs
#4,333,306
of 18,034,577 outputs
Outputs from BMC Genomics
#1,876
of 9,513 outputs
Outputs of similar age
#54,782
of 233,239 outputs
Outputs of similar age from BMC Genomics
#1
of 1 outputs
Altmetric has tracked 18,034,577 research outputs across all sources so far. Compared to these this one has done well and is in the 75th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 9,513 research outputs from this source. They receive a mean Attention Score of 4.4. This one has done well, scoring higher than 80% 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 233,239 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 76% of its contemporaries.
We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them