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Integrative network analysis of TCGA data for ovarian cancer

Overview of attention for article published in BMC Systems Biology, December 2014
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About this Attention Score

  • Good Attention Score compared to outputs of the same age (73rd percentile)
  • High Attention Score compared to outputs of the same age and source (84th percentile)

Mentioned by

twitter
6 tweeters
googleplus
1 Google+ user

Citations

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55 Dimensions

Readers on

mendeley
101 Mendeley
citeulike
5 CiteULike
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Title
Integrative network analysis of TCGA data for ovarian cancer
Published in
BMC Systems Biology, December 2014
DOI 10.1186/s12918-014-0136-9
Pubmed ID
Authors

Qingyang Zhang, Joanna E Burdette, Ji-Ping Wang

Abstract

BackgroundOver the past years, tremendous efforts have been made to elucidate the molecular basis of the initiation and progression of ovarian cancer. However, most existing studies have been focused on individual genes or a single type of data, which may lack the power to detect the complex mechanism of cancer formation by overlooking the interactions of different genetic and epigenetic factors.ResultsWe propose an integrative framework to identify genetic and epigenetic features related to ovarian cancer and to quantify the causal relationships among these features using a probabilistic graphical model based on the Cancer Genome Atlas (TCGA) data. In the feature selection, we first defined a set of seed genes by including 48 candidate tumor suppressors or oncogenes and an additional 20 ovarian cancer related genes reported in the literature. The seed genes were then fed into a stepwise correlation-based selector to identify 271 additional features including 177 genes, 82 copy number variation sites, 11 methylation sites and 1 somatic mutation (at gene TP53). We built a Bayesian network model with a logit link function to quantify the causal relationship among these features and discovered a set of 13 hub genes including ARID1A, C19orf53, CSKN2A1 and COL5A2. The directed graph revealed many potential genetic pathways, some of which confirmed the existing results in the literature. Clustering analysis further suggested four gene clusters, three of which correspond to well-defined cellular processes including cell division, tumor invasion and mitochondrial system. In addition, two genes related to glycoprotein synthesis, PSG11 and GALNT10, were found highly predictive for the overall survival time of ovarian cancer patients.ConclusionsThe proposed framework is effective in identifying possible important genetic and epigenetic features that are related to complex cancer diseases. The constructed Bayesian network has identified some new genetic/epigenetic pathways, which may shed new light into the molecular mechanisms of ovarian cancer.

Twitter Demographics

The data shown below were collected from the profiles of 6 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 101 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 1 <1%
Brazil 1 <1%
Sweden 1 <1%
United Kingdom 1 <1%
Spain 1 <1%
United States 1 <1%
Unknown 95 94%

Demographic breakdown

Readers by professional status Count As %
Researcher 25 25%
Student > Ph. D. Student 17 17%
Student > Bachelor 14 14%
Student > Master 10 10%
Student > Postgraduate 6 6%
Other 16 16%
Unknown 13 13%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 25 25%
Agricultural and Biological Sciences 24 24%
Medicine and Dentistry 12 12%
Computer Science 8 8%
Engineering 5 5%
Other 11 11%
Unknown 16 16%

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 22 December 2015.
All research outputs
#3,407,237
of 12,537,999 outputs
Outputs from BMC Systems Biology
#235
of 1,056 outputs
Outputs of similar age
#73,441
of 285,211 outputs
Outputs of similar age from BMC Systems Biology
#13
of 82 outputs
Altmetric has tracked 12,537,999 research outputs across all sources so far. This one has received more attention than most of these and is in the 72nd percentile.
So far Altmetric has tracked 1,056 research outputs from this source. They receive a mean Attention Score of 3.4. This one has done well, scoring higher than 76% 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 285,211 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 73% of its contemporaries.
We're also able to compare this research output to 82 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 84% of its contemporaries.