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A network model for angiogenesis in ovarian cancer

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

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (86th percentile)
  • High Attention Score compared to outputs of the same age and source (88th percentile)

Mentioned by

blogs
1 blog
twitter
12 X users

Citations

dimensions_citation
63 Dimensions

Readers on

mendeley
92 Mendeley
citeulike
2 CiteULike
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Title
A network model for angiogenesis in ovarian cancer
Published in
BMC Bioinformatics, April 2015
DOI 10.1186/s12859-015-0551-y
Pubmed ID
Authors

Kimberly Glass, John Quackenbush, Dimitrios Spentzos, Benjamin Haibe-Kains, Guo-Cheng Yuan

Abstract

We recently identified two robust ovarian cancer subtypes, defined by the expression of genes involved in angiogenesis, with significant differences in clinical outcome. To identify potential regulatory mechanisms that distinguish the subtypes we applied PANDA, a method that uses an integrative approach to model information flow in gene regulatory networks. We find distinct differences between networks that are active in the angiogenic and non-angiogenic subtypes, largely defined by a set of key transcription factors that, although previously reported to play a role in angiogenesis, are not strongly differentially-expressed between the subtypes. Our network analysis indicates that these factors are involved in the activation (or repression) of different genes in the two subtypes, resulting in differential expression of their network targets. Mechanisms mediating differences between subtypes include a previously unrecognized pro-angiogenic role for increased genome-wide DNA methylation and complex patterns of combinatorial regulation. The models we develop require a shift in our interpretation of the driving factors in biological networks away from the genes themselves and toward their interactions. The observed regulatory changes between subtypes suggest therapeutic interventions that may help in the treatment of ovarian cancer.

X Demographics

X Demographics

The data shown below were collected from the profiles of 12 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 92 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 2 2%
United States 2 2%
Belgium 2 2%
Unknown 86 93%

Demographic breakdown

Readers by professional status Count As %
Researcher 31 34%
Student > Ph. D. Student 16 17%
Student > Master 5 5%
Professor 4 4%
Student > Doctoral Student 4 4%
Other 14 15%
Unknown 18 20%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 18 20%
Medicine and Dentistry 15 16%
Agricultural and Biological Sciences 14 15%
Mathematics 7 8%
Computer Science 6 7%
Other 10 11%
Unknown 22 24%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 12. 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 12 January 2016.
All research outputs
#2,556,305
of 22,799,071 outputs
Outputs from BMC Bioinformatics
#791
of 7,281 outputs
Outputs of similar age
#34,641
of 264,708 outputs
Outputs of similar age from BMC Bioinformatics
#16
of 135 outputs
Altmetric has tracked 22,799,071 research outputs across all sources so far. Compared to these this one has done well and is in the 88th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,281 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 done well, scoring higher than 89% 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 264,708 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 86% of its contemporaries.
We're also able to compare this research output to 135 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 88% of its contemporaries.