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Using large-scale genomics data to identify driver mutations in lung cancer: methods and challenges

Overview of attention for article published in Pharmacogenomics, July 2015
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Among the highest-scoring outputs from this source (#16 of 938)
  • High Attention Score compared to outputs of the same age (90th percentile)
  • High Attention Score compared to outputs of the same age and source (92nd percentile)

Mentioned by

blogs
1 blog
twitter
17 tweeters
facebook
1 Facebook page
reddit
1 Redditor

Citations

dimensions_citation
12 Dimensions

Readers on

mendeley
20 Mendeley
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Title
Using large-scale genomics data to identify driver mutations in lung cancer: methods and challenges
Published in
Pharmacogenomics, July 2015
DOI 10.2217/pgs.15.60
Pubmed ID
Authors

Andrew M Hudson, Christopher Wirth, Natalie L Stephenson, Shameem Fawdar, John Brognard, Crispin J Miller

Abstract

Lung cancer is the commonest cause of cancer death in the world and carries a poor prognosis for most patients. While precision targeting of mutated proteins has given some successes for never- and light-smoking patients, there are no proven targeted therapies for the majority of smokers with the disease. Despite sequencing hundreds of lung cancers, known driver mutations are lacking for a majority of tumors. Distinguishing driver mutations from inconsequential passenger mutations in a given lung tumor is extremely challenging due to the high mutational burden of smoking-related cancers. Here we discuss the methods employed to identify driver mutations from these large datasets. We examine different approaches based on bioinformatics, in silico structural modeling and biological dependency screens and discuss the limitations of these approaches.

Twitter Demographics

The data shown below were collected from the profiles of 17 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 20 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 20 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 4 20%
Student > Bachelor 3 15%
Student > Ph. D. Student 3 15%
Unspecified 1 5%
Other 1 5%
Other 3 15%
Unknown 5 25%
Readers by discipline Count As %
Medicine and Dentistry 4 20%
Agricultural and Biological Sciences 4 20%
Nursing and Health Professions 2 10%
Biochemistry, Genetics and Molecular Biology 2 10%
Computer Science 2 10%
Other 2 10%
Unknown 4 20%

Attention Score in Context

This research output has an Altmetric Attention Score of 18. 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 October 2015.
All research outputs
#1,202,933
of 16,248,698 outputs
Outputs from Pharmacogenomics
#16
of 938 outputs
Outputs of similar age
#21,462
of 237,401 outputs
Outputs of similar age from Pharmacogenomics
#2
of 26 outputs
Altmetric has tracked 16,248,698 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 92nd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 938 research outputs from this source. They receive a mean Attention Score of 3.6. This one has done particularly well, scoring higher than 98% 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 237,401 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 90% of its contemporaries.
We're also able to compare this research output to 26 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 92% of its contemporaries.