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A multilevel pan-cancer map links gene mutations to cancer hallmarks

Overview of attention for article published in Ai zheng Aizheng Chinese journal of cancer, September 2015
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  • Above-average Attention Score compared to outputs of the same age and source (53rd percentile)

Mentioned by

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1 tweeter
googleplus
1 Google+ user

Citations

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19 Dimensions

Readers on

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57 Mendeley
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Title
A multilevel pan-cancer map links gene mutations to cancer hallmarks
Published in
Ai zheng Aizheng Chinese journal of cancer, September 2015
DOI 10.1186/s40880-015-0050-6
Pubmed ID
Authors

Theo A. Knijnenburg, Tycho Bismeijer, Lodewyk F. A. Wessels, Ilya Shmulevich

Abstract

A central challenge in cancer research is to create models that bridge the gap between the molecular level on which interventions can be designed and the cellular and tissue levels on which the disease phenotypes are manifested. This study was undertaken to construct such a model from functional annotations and explore its use when integrated with large-scale cancer genomics data. We created a map that connects genes to cancer hallmarks via signaling pathways. We projected gene mutation and focal copy number data from various cancer types onto this map. We performed statistical analyses to uncover mutually exclusive and co-occurring oncogenic aberrations within this topology. Our analysis showed that although the genetic fingerprint of tumor types could be very different, there were less variations at the level of hallmarks, consistent with the idea that different genetic alterations have similar functional outcomes. Additionally, we showed how the multilevel map could help to clarify the role of infrequently mutated genes, and we demonstrated that mutually exclusive gene mutations were more prevalent in pathways, whereas many co-occurring gene mutations were associated with hallmark characteristics. Overlaying this map with gene mutation and focal copy number data from various cancer types makes it possible to investigate the similarities and differences between tumor samples systematically at the levels of not only genes but also pathways and hallmarks.

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter 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 57 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Spain 1 2%
Denmark 1 2%
Unknown 55 96%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 16 28%
Researcher 10 18%
Student > Bachelor 9 16%
Student > Master 4 7%
Student > Doctoral Student 3 5%
Other 9 16%
Unknown 6 11%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 18 32%
Agricultural and Biological Sciences 14 25%
Computer Science 10 18%
Medicine and Dentistry 5 9%
Pharmacology, Toxicology and Pharmaceutical Science 2 4%
Other 4 7%
Unknown 4 7%

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 19 September 2015.
All research outputs
#14,238,195
of 22,829,083 outputs
Outputs from Ai zheng Aizheng Chinese journal of cancer
#155
of 264 outputs
Outputs of similar age
#138,854
of 268,597 outputs
Outputs of similar age from Ai zheng Aizheng Chinese journal of cancer
#6
of 13 outputs
Altmetric has tracked 22,829,083 research outputs across all sources so far. This one is in the 35th percentile – i.e., 35% of other outputs scored the same or lower than it.
So far Altmetric has tracked 264 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.2. This one is in the 40th percentile – i.e., 40% 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 268,597 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 45th percentile – i.e., 45% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 13 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 53% of its contemporaries.