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A Multi-Method Approach for Proteomic Network Inference in 11 Human Cancers

Overview of attention for article published in PLoS Computational Biology, February 2016
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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)
  • Good Attention Score compared to outputs of the same age and source (66th percentile)

Mentioned by

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19 X users
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1 Facebook page
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3 Google+ users

Citations

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31 Dimensions

Readers on

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83 Mendeley
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4 CiteULike
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Title
A Multi-Method Approach for Proteomic Network Inference in 11 Human Cancers
Published in
PLoS Computational Biology, February 2016
DOI 10.1371/journal.pcbi.1004765
Pubmed ID
Authors

Yasin Şenbabaoğlu, Selçuk Onur Sümer, Francisco Sánchez-Vega, Debra Bemis, Giovanni Ciriello, Nikolaus Schultz, Chris Sander

Abstract

Protein expression and post-translational modification levels are tightly regulated in neoplastic cells to maintain cellular processes known as 'cancer hallmarks'. The first Pan-Cancer initiative of The Cancer Genome Atlas (TCGA) Research Network has aggregated protein expression profiles for 3,467 patient samples from 11 tumor types using the antibody based reverse phase protein array (RPPA) technology. The resultant proteomic data can be utilized to computationally infer protein-protein interaction (PPI) networks and to study the commonalities and differences across tumor types. In this study, we compare the performance of 13 established network inference methods in their capacity to retrieve the curated Pathway Commons interactions from RPPA data. We observe that no single method has the best performance in all tumor types, but a group of six methods, including diverse techniques such as correlation, mutual information, and regression, consistently rank highly among the tested methods. We utilize the high performing methods to obtain a consensus network; and identify four robust and densely connected modules that reveal biological processes as well as suggest antibody-related technical biases. Mapping the consensus network interactions to Reactome gene lists confirms the pan-cancer importance of signal transduction pathways, innate and adaptive immune signaling, cell cycle, metabolism, and DNA repair; and also suggests several biological processes that may be specific to a subset of tumor types. Our results illustrate the utility of the RPPA platform as a tool to study proteomic networks in cancer.

X Demographics

X Demographics

The data shown below were collected from the profiles of 19 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 83 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Italy 3 4%
United States 2 2%
United Kingdom 1 1%
Germany 1 1%
Unknown 76 92%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 22 27%
Researcher 22 27%
Student > Bachelor 10 12%
Student > Master 6 7%
Professor > Associate Professor 5 6%
Other 8 10%
Unknown 10 12%
Readers by discipline Count As %
Agricultural and Biological Sciences 24 29%
Biochemistry, Genetics and Molecular Biology 14 17%
Computer Science 9 11%
Medicine and Dentistry 7 8%
Engineering 5 6%
Other 10 12%
Unknown 14 17%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 13. 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 15 July 2016.
All research outputs
#2,707,156
of 25,374,917 outputs
Outputs from PLoS Computational Biology
#2,430
of 8,960 outputs
Outputs of similar age
#41,618
of 312,040 outputs
Outputs of similar age from PLoS Computational Biology
#56
of 167 outputs
Altmetric has tracked 25,374,917 research outputs across all sources so far. Compared to these this one has done well and is in the 89th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 8,960 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 20.4. This one has gotten more attention than average, scoring higher than 72% 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 312,040 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 167 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 66% of its contemporaries.