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Getting personalized cancer genome analysis into the clinic: the challenges in bioinformatics

Overview of attention for article published in Genome Medicine, July 2012
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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 (89th percentile)
  • Above-average Attention Score compared to outputs of the same age and source (54th percentile)

Mentioned by

blogs
1 blog
twitter
4 tweeters

Citations

dimensions_citation
26 Dimensions

Readers on

mendeley
100 Mendeley
citeulike
1 CiteULike
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Title
Getting personalized cancer genome analysis into the clinic: the challenges in bioinformatics
Published in
Genome Medicine, July 2012
DOI 10.1186/gm362
Pubmed ID
Authors

Alfonso Valencia, Manuel Hidalgo

Abstract

Progress in genomics has raised expectations in many fields, and particularly in personalized cancer research. The new technologies available make it possible to combine information about potential disease markers, altered function and accessible drug targets, which, coupled with pathological and medical information, will help produce more appropriate clinical decisions. The accessibility of such experimental techniques makes it all the more necessary to improve and adapt computational strategies to the new challenges. This review focuses on the critical issues associated with the standard pipeline, which includes: DNA sequencing analysis; analysis of mutations in coding regions; the study of genome rearrangements; extrapolating information on mutations to the functional and signaling level; and predicting the effects of therapies using mouse tumor models. We describe the possibilities, limitations and future challenges of current bioinformatics strategies for each of these issues. Furthermore, we emphasize the need for the collaboration between the bioinformaticians who implement the software and use the data resources, the computational biologists who develop the analytical methods, and the clinicians, the systems' end users and those ultimately responsible for taking medical decisions. Finally, the different steps in cancer genome analysis are illustrated through examples of applications in cancer genome analysis.

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 100 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 4 4%
Spain 2 2%
United Kingdom 1 1%
Australia 1 1%
France 1 1%
Unknown 91 91%

Demographic breakdown

Readers by professional status Count As %
Researcher 27 27%
Student > Ph. D. Student 16 16%
Student > Bachelor 12 12%
Professor 10 10%
Student > Doctoral Student 7 7%
Other 19 19%
Unknown 9 9%
Readers by discipline Count As %
Agricultural and Biological Sciences 36 36%
Biochemistry, Genetics and Molecular Biology 18 18%
Medicine and Dentistry 13 13%
Computer Science 13 13%
Nursing and Health Professions 2 2%
Other 10 10%
Unknown 8 8%

Attention Score in Context

This research output has an Altmetric Attention Score of 10. 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 23 October 2014.
All research outputs
#831,365
of 8,024,804 outputs
Outputs from Genome Medicine
#281
of 717 outputs
Outputs of similar age
#10,065
of 96,628 outputs
Outputs of similar age from Genome Medicine
#5
of 11 outputs
Altmetric has tracked 8,024,804 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 717 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 19.1. This one has gotten more attention than average, scoring higher than 60% 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 96,628 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 89% of its contemporaries.
We're also able to compare this research output to 11 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 54% of its contemporaries.