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Tumor characterization and stratification by integrated molecular profiles reveals essential pan-cancer features

Overview of attention for article published in BMC Genomics, July 2015
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52 Mendeley
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Title
Tumor characterization and stratification by integrated molecular profiles reveals essential pan-cancer features
Published in
BMC Genomics, July 2015
DOI 10.1186/s12864-015-1687-x
Pubmed ID
Authors

Zhaoqi Liu, Shihua Zhang

Abstract

Identification of tumor heterogeneity and genomic similarities across different cancer types is essential to the design of effective stratified treatments and for the discovery of treatments that can be extended to different types of tumors. However, systematic investigations on comprehensive molecular profiles have not been fully explored to achieve this goal. Here, we performed a network-based integrative pan-cancer genomic analysis on >3000 samples from 12 cancer types to uncover novel stratifications among tumors. Our study not only revealed recurrently reported cross-cancer similarities, but also identified novel ones. The macro-scale stratification demonstrates strong clinical relevance and reveals consistent risk tendency among cancer types. The micro-scale stratification shows essential pan-cancer heterogeneity with subgroup-specific gene network characteristics and biological functions. In summary, our comprehensive network-based pan-cancer stratification provides valuable information about inter- and intra- cancer stratification for patient clinical assessments and therapeutic strategies.

X Demographics

X Demographics

The data shown below were collected from the profiles of 5 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 52 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Spain 1 2%
Netherlands 1 2%
Unknown 50 96%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 13 25%
Researcher 9 17%
Student > Master 7 13%
Student > Bachelor 4 8%
Student > Doctoral Student 2 4%
Other 7 13%
Unknown 10 19%
Readers by discipline Count As %
Agricultural and Biological Sciences 13 25%
Biochemistry, Genetics and Molecular Biology 11 21%
Engineering 5 10%
Medicine and Dentistry 4 8%
Computer Science 3 6%
Other 4 8%
Unknown 12 23%
Attention Score in Context

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 08 March 2016.
All research outputs
#14,102,908
of 23,881,329 outputs
Outputs from BMC Genomics
#5,162
of 10,793 outputs
Outputs of similar age
#126,299
of 264,667 outputs
Outputs of similar age from BMC Genomics
#137
of 254 outputs
Altmetric has tracked 23,881,329 research outputs across all sources so far. This one is in the 39th percentile – i.e., 39% of other outputs scored the same or lower than it.
So far Altmetric has tracked 10,793 research outputs from this source. They receive a mean Attention Score of 4.8. This one is in the 49th percentile – i.e., 49% 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 264,667 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 50% of its contemporaries.
We're also able to compare this research output to 254 others from the same source and published within six weeks on either side of this one. This one is in the 44th percentile – i.e., 44% of its contemporaries scored the same or lower than it.