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Integration of multiple networks and pathways identifies cancer driver genes in pan-cancer analysis

Overview of attention for article published in BMC Genomics, January 2018
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (54th percentile)
  • Above-average Attention Score compared to outputs of the same age and source (53rd percentile)

Mentioned by

twitter
4 tweeters

Citations

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27 Dimensions

Readers on

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64 Mendeley
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Title
Integration of multiple networks and pathways identifies cancer driver genes in pan-cancer analysis
Published in
BMC Genomics, January 2018
DOI 10.1186/s12864-017-4423-x
Pubmed ID
Authors

Claudia Cava, Gloria Bertoli, Antonio Colaprico, Catharina Olsen, Gianluca Bontempi, Isabella Castiglioni

Abstract

Modern high-throughput genomic technologies represent a comprehensive hallmark of molecular changes in pan-cancer studies. Although different cancer gene signatures have been revealed, the mechanism of tumourigenesis has yet to be completely understood. Pathways and networks are important tools to explain the role of genes in functional genomic studies. However, few methods consider the functional non-equal roles of genes in pathways and the complex gene-gene interactions in a network. We present a novel method in pan-cancer analysis that identifies de-regulated genes with a functional role by integrating pathway and network data. A pan-cancer analysis of 7158 tumour/normal samples from 16 cancer types identified 895 genes with a central role in pathways and de-regulated in cancer. Comparing our approach with 15 current tools that identify cancer driver genes, we found that 35.6% of the 895 genes identified by our method have been found as cancer driver genes with at least 2/15 tools. Finally, we applied a machine learning algorithm on 16 independent GEO cancer datasets to validate the diagnostic role of cancer driver genes for each cancer. We obtained a list of the top-ten cancer driver genes for each cancer considered in this study. Our analysis 1) confirmed that there are several known cancer driver genes in common among different types of cancer, 2) highlighted that cancer driver genes are able to regulate crucial pathways.

Twitter Demographics

The data shown below were collected from the profiles of 4 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 64 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 64 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 15 23%
Student > Ph. D. Student 13 20%
Student > Bachelor 8 13%
Student > Master 7 11%
Student > Postgraduate 4 6%
Other 6 9%
Unknown 11 17%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 18 28%
Agricultural and Biological Sciences 9 14%
Computer Science 8 13%
Medicine and Dentistry 7 11%
Chemistry 2 3%
Other 8 13%
Unknown 12 19%

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 10 September 2018.
All research outputs
#8,651,260
of 15,707,853 outputs
Outputs from BMC Genomics
#3,992
of 8,797 outputs
Outputs of similar age
#178,894
of 406,810 outputs
Outputs of similar age from BMC Genomics
#356
of 823 outputs
Altmetric has tracked 15,707,853 research outputs across all sources so far. This one is in the 43rd percentile – i.e., 43% of other outputs scored the same or lower than it.
So far Altmetric has tracked 8,797 research outputs from this source. They receive a mean Attention Score of 4.3. This one has gotten more attention than average, scoring higher than 51% 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 406,810 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 54% of its contemporaries.
We're also able to compare this research output to 823 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.