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X Demographics
Mendeley readers
Attention Score in Context
Title |
Multi-scale agent-based brain cancer modeling and prediction of TKI treatment response: Incorporating EGFR signaling pathway and angiogenesis
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Published in |
BMC Bioinformatics, August 2012
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DOI | 10.1186/1471-2105-13-218 |
Pubmed ID | |
Authors |
Xiaoqiang Sun, Le Zhang, Hua Tan, Jiguang Bao, Costas Strouthos, Xiaobo Zhou |
Abstract |
The epidermal growth factor receptor (EGFR) signaling pathway and angiogenesis in brain cancer act as an engine for tumor initiation, expansion and response to therapy. Since the existing literature does not have any models that investigate the impact of both angiogenesis and molecular signaling pathways on treatment, we propose a novel multi-scale, agent-based computational model that includes both angiogenesis and EGFR modules to study the response of brain cancer under tyrosine kinase inhibitors (TKIs) treatment. |
X Demographics
The data shown below were collected from the profiles of 3 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 3 | 100% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 3 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 77 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 1 | 1% |
France | 1 | 1% |
Unknown | 75 | 97% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 24 | 31% |
Researcher | 14 | 18% |
Student > Master | 7 | 9% |
Student > Bachelor | 4 | 5% |
Student > Doctoral Student | 4 | 5% |
Other | 12 | 16% |
Unknown | 12 | 16% |
Readers by discipline | Count | As % |
---|---|---|
Computer Science | 15 | 19% |
Engineering | 12 | 16% |
Agricultural and Biological Sciences | 9 | 12% |
Biochemistry, Genetics and Molecular Biology | 9 | 12% |
Mathematics | 6 | 8% |
Other | 14 | 18% |
Unknown | 12 | 16% |
Attention Score in Context
This research output has an Altmetric Attention Score of 3. 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 01 September 2012.
All research outputs
#13,134,992
of 22,675,759 outputs
Outputs from BMC Bioinformatics
#3,999
of 7,249 outputs
Outputs of similar age
#92,281
of 169,692 outputs
Outputs of similar age from BMC Bioinformatics
#47
of 99 outputs
Altmetric has tracked 22,675,759 research outputs across all sources so far. This one is in the 41st percentile – i.e., 41% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,249 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one is in the 42nd percentile – i.e., 42% of its peers scored the same or lower than it.
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 169,692 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 45th percentile – i.e., 45% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 99 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 51% of its contemporaries.