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Personalized Oncology Suite: integrating next-generation sequencing data and whole-slide bioimages

Overview of attention for article published in BMC Bioinformatics, September 2014
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About this Attention Score

  • Good Attention Score compared to outputs of the same age (76th percentile)
  • Above-average Attention Score compared to outputs of the same age and source (63rd percentile)

Mentioned by

twitter
8 X users

Citations

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10 Dimensions

Readers on

mendeley
58 Mendeley
citeulike
2 CiteULike
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Title
Personalized Oncology Suite: integrating next-generation sequencing data and whole-slide bioimages
Published in
BMC Bioinformatics, September 2014
DOI 10.1186/1471-2105-15-306
Pubmed ID
Authors

Andreas Dander, Matthias Baldauf, Michael Sperk, Stephan Pabinger, Benjamin Hiltpolt, Zlatko Trajanoski

Abstract

Cancer immunotherapy has recently entered a remarkable renaissance phase with the approval of several agents for treatment. Cancer treatment platforms have demonstrated profound tumor regressions including complete cure in patients with metastatic cancer. Moreover, technological advances in next-generation sequencing (NGS) as well as the development of devices for scanning whole-slide bioimages from tissue sections and image analysis software for quantitation of tumor-infiltrating lymphocytes (TILs) allow, for the first time, the development of personalized cancer immunotherapies that target patient specific mutations. However, there is currently no bioinformatics solution that supports the integration of these heterogeneous datasets.

X Demographics

X Demographics

The data shown below were collected from the profiles of 8 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 58 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
India 1 2%
Sweden 1 2%
Unknown 56 97%

Demographic breakdown

Readers by professional status Count As %
Researcher 16 28%
Student > Ph. D. Student 7 12%
Student > Master 7 12%
Student > Bachelor 4 7%
Professor 3 5%
Other 14 24%
Unknown 7 12%
Readers by discipline Count As %
Computer Science 11 19%
Medicine and Dentistry 11 19%
Agricultural and Biological Sciences 9 16%
Biochemistry, Genetics and Molecular Biology 5 9%
Engineering 3 5%
Other 9 16%
Unknown 10 17%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 5. 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 26 November 2014.
All research outputs
#5,877,602
of 23,577,654 outputs
Outputs from BMC Bioinformatics
#2,129
of 7,400 outputs
Outputs of similar age
#59,141
of 251,352 outputs
Outputs of similar age from BMC Bioinformatics
#40
of 111 outputs
Altmetric has tracked 23,577,654 research outputs across all sources so far. This one has received more attention than most of these and is in the 74th percentile.
So far Altmetric has tracked 7,400 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one has gotten more attention than average, scoring higher than 70% 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 251,352 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 76% of its contemporaries.
We're also able to compare this research output to 111 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 63% of its contemporaries.