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In silico modeling predicts drug sensitivity of patient-derived cancer cells

Overview of attention for article published in Journal of Translational Medicine, May 2014
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (95th percentile)

Mentioned by

news
3 news outlets
blogs
1 blog
twitter
12 tweeters
facebook
2 Facebook pages
googleplus
4 Google+ users
reddit
1 Redditor

Citations

dimensions_citation
24 Dimensions

Readers on

mendeley
72 Mendeley
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Title
In silico modeling predicts drug sensitivity of patient-derived cancer cells
Published in
Journal of Translational Medicine, May 2014
DOI 10.1186/1479-5876-12-128
Pubmed ID
Authors

Sandeep C Pingle, Zeba Sultana, Sandra Pastorino, Pengfei Jiang, Rajesh Mukthavaram, Ying Chao, Ila Sri Bharati, Natsuko Nomura, Milan Makale, Taher Abbasi, Shweta Kapoor, Ansu Kumar, Shahabuddin Usmani, Ashish Agrawal, Shireen Vali, Santosh Kesari

Abstract

Glioblastoma (GBM) is an aggressive disease associated with poor survival. It is essential to account for the complexity of GBM biology to improve diagnostic and therapeutic strategies. This complexity is best represented by the increasing amounts of profiling ("omics") data available due to advances in biotechnology. The challenge of integrating these vast genomic and proteomic data can be addressed by a comprehensive systems modeling approach.

Twitter Demographics

The data shown below were collected from the profiles of 12 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 72 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 2 3%
Unknown 70 97%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 18 25%
Student > Bachelor 11 15%
Researcher 11 15%
Other 6 8%
Student > Postgraduate 6 8%
Other 12 17%
Unknown 8 11%
Readers by discipline Count As %
Agricultural and Biological Sciences 15 21%
Biochemistry, Genetics and Molecular Biology 12 17%
Engineering 9 13%
Computer Science 8 11%
Medicine and Dentistry 7 10%
Other 11 15%
Unknown 10 14%

Attention Score in Context

This research output has an Altmetric Attention Score of 37. 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 07 November 2018.
All research outputs
#756,304
of 19,192,351 outputs
Outputs from Journal of Translational Medicine
#130
of 3,413 outputs
Outputs of similar age
#8,875
of 199,752 outputs
Outputs of similar age from Journal of Translational Medicine
#1
of 1 outputs
Altmetric has tracked 19,192,351 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 96th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 3,413 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 9.3. This one has done particularly well, scoring higher than 96% 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 199,752 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 95% of its contemporaries.
We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them