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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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  • Good Attention Score compared to outputs of the same age (74th percentile)
  • Good Attention Score compared to outputs of the same age and source (74th percentile)

Mentioned by

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3 X users
patent
2 patents
facebook
1 Facebook page

Citations

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31 Dimensions

Readers on

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60 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.

X Demographics

X Demographics

The data shown below were collected from the profiles of 3 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 60 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 57 95%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 19 32%
Researcher 14 23%
Student > Bachelor 7 12%
Student > Master 6 10%
Student > Doctoral Student 2 3%
Other 5 8%
Unknown 7 12%
Readers by discipline Count As %
Agricultural and Biological Sciences 12 20%
Biochemistry, Genetics and Molecular Biology 10 17%
Computer Science 10 17%
Medicine and Dentistry 8 13%
Engineering 5 8%
Other 6 10%
Unknown 9 15%
Attention Score in Context

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 10 August 2023.
All research outputs
#6,181,486
of 24,387,992 outputs
Outputs from BMC Genomics
#2,437
of 10,969 outputs
Outputs of similar age
#68,251
of 268,597 outputs
Outputs of similar age from BMC Genomics
#72
of 274 outputs
Altmetric has tracked 24,387,992 research outputs across all sources so far. This one has received more attention than most of these and is in the 74th percentile.
So far Altmetric has tracked 10,969 research outputs from this source. They receive a mean Attention Score of 4.8. This one has done well, scoring higher than 77% 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 268,597 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 74% of its contemporaries.
We're also able to compare this research output to 274 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 74% of its contemporaries.