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Mendeley readers
Attention Score in Context
Title |
In silico modeling predicts drug sensitivity of patient-derived cancer cells
|
---|---|
Published in |
Journal of Translational Medicine, May 2014
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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. |
X Demographics
The data shown below were collected from the profiles of 13 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 4 | 31% |
India | 2 | 15% |
Australia | 2 | 15% |
United Kingdom | 1 | 8% |
Germany | 1 | 8% |
Unknown | 3 | 23% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 10 | 77% |
Scientists | 2 | 15% |
Science communicators (journalists, bloggers, editors) | 1 | 8% |
Mendeley readers
The data shown below were compiled from readership statistics for 78 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
Country | Count | As % |
---|---|---|
United Kingdom | 2 | 3% |
Unknown | 76 | 97% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 18 | 23% |
Student > Bachelor | 12 | 15% |
Researcher | 11 | 14% |
Other | 6 | 8% |
Student > Postgraduate | 6 | 8% |
Other | 13 | 17% |
Unknown | 12 | 15% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 15 | 19% |
Biochemistry, Genetics and Molecular Biology | 12 | 15% |
Engineering | 9 | 12% |
Medicine and Dentistry | 9 | 12% |
Computer Science | 8 | 10% |
Other | 13 | 17% |
Unknown | 12 | 15% |
Attention Score in Context
This research output has an Altmetric Attention Score of 38. 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
#909,096
of 22,756,196 outputs
Outputs from Journal of Translational Medicine
#163
of 3,978 outputs
Outputs of similar age
#9,697
of 226,345 outputs
Outputs of similar age from Journal of Translational Medicine
#1
of 70 outputs
Altmetric has tracked 22,756,196 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 3,978 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 10.5. This one has done particularly well, scoring higher than 95% 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 226,345 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 70 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 98% of its contemporaries.