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Extracting a low-dimensional description of multiple gene expression datasets reveals a potential driver for tumor-associated stroma in ovarian cancer

Overview of attention for article published in Genome Medicine, June 2016
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  • Good Attention Score compared to outputs of the same age (67th percentile)

Mentioned by

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9 X users

Citations

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19 Dimensions

Readers on

mendeley
59 Mendeley
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1 CiteULike
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Title
Extracting a low-dimensional description of multiple gene expression datasets reveals a potential driver for tumor-associated stroma in ovarian cancer
Published in
Genome Medicine, June 2016
DOI 10.1186/s13073-016-0319-7
Pubmed ID
Authors

Safiye Celik, Benjamin A. Logsdon, Stephanie Battle, Charles W. Drescher, Mara Rendi, R. David Hawkins, Su-In Lee

Abstract

Patterns in expression data conserved across multiple independent disease studies are likely to represent important molecular events underlying the disease. We present the INSPIRE method to infer modules of co-expressed genes and the dependencies among the modules from multiple expression datasets that may contain different sets of genes. We show that INSPIRE infers more accurate models than existing methods to extract low-dimensional representation of expression data. We demonstrate that applying INSPIRE to nine ovarian cancer datasets leads to a new marker and potential driver of tumor-associated stroma, HOPX, followed by experimental validation. The implementation of INSPIRE is available at http://inspire.cs.washington.edu .

X Demographics

X Demographics

The data shown below were collected from the profiles of 9 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 59 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 3%
Netherlands 1 2%
Luxembourg 1 2%
Unknown 55 93%

Demographic breakdown

Readers by professional status Count As %
Researcher 15 25%
Student > Ph. D. Student 14 24%
Other 7 12%
Student > Bachelor 4 7%
Student > Doctoral Student 3 5%
Other 9 15%
Unknown 7 12%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 12 20%
Agricultural and Biological Sciences 11 19%
Medicine and Dentistry 11 19%
Computer Science 9 15%
Engineering 5 8%
Other 4 7%
Unknown 7 12%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 4. 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 04 May 2017.
All research outputs
#6,814,845
of 22,877,793 outputs
Outputs from Genome Medicine
#1,080
of 1,443 outputs
Outputs of similar age
#109,833
of 345,199 outputs
Outputs of similar age from Genome Medicine
#25
of 27 outputs
Altmetric has tracked 22,877,793 research outputs across all sources so far. This one has received more attention than most of these and is in the 69th percentile.
So far Altmetric has tracked 1,443 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 25.8. This one is in the 24th percentile – i.e., 24% 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 345,199 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 67% of its contemporaries.
We're also able to compare this research output to 27 others from the same source and published within six weeks on either side of this one. This one is in the 7th percentile – i.e., 7% of its contemporaries scored the same or lower than it.