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Mendeley readers
Attention Score in Context
| Title |
Rule-based multi-scale simulation for drug effect pathway analysis
|
|---|---|
| Published in |
BMC Medical Informatics and Decision Making, April 2013
|
| DOI | 10.1186/1472-6947-13-s1-s4 |
| Pubmed ID | |
| Authors |
Woochang Hwang, Yongdeuk Hwang, Sunjae Lee, Doheon Lee |
| Abstract |
Biological systems are robust and complex to maintain stable phenotypes under various conditions. In these systems, drugs reported the limited efficacy and unexpected side-effects. To remedy this situation, many pharmaceutical laboratories have begun to research combination drugs and some of them have shown successful clinical results. Complementary action of multiple compounds could increase efficacy as well as reduce side-effects through pharmacological interactions. However, experimental approach requires vast cost of preclinical experiments and tests as the number of possible combinations of compound dosages increases exponentially. Computer model-based experiments have been emerging as one of the most promising solutions to cope with such complexity. Though there have been many efforts to model specific molecular pathways using qualitative and quantitative formalisms, they suffer from unexpected results caused by distant interactions beyond their localized models. |
Mendeley readers
The data shown below were compiled from readership statistics for 39 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 1 | 3% |
| United States | 1 | 3% |
| Unknown | 37 | 95% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 11 | 28% |
| Student > Ph. D. Student | 9 | 23% |
| Student > Doctoral Student | 6 | 15% |
| Unspecified | 2 | 5% |
| Student > Postgraduate | 2 | 5% |
| Other | 4 | 10% |
| Unknown | 5 | 13% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 7 | 18% |
| Biochemistry, Genetics and Molecular Biology | 6 | 15% |
| Computer Science | 5 | 13% |
| Engineering | 4 | 10% |
| Agricultural and Biological Sciences | 3 | 8% |
| Other | 8 | 21% |
| Unknown | 6 | 15% |
Attention Score in Context
This research output has an Altmetric Attention Score of 12. 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 08 July 2013.
All research outputs
#2,582,662
of 22,713,403 outputs
Outputs from BMC Medical Informatics and Decision Making
#187
of 1,982 outputs
Outputs of similar age
#22,484
of 199,934 outputs
Outputs of similar age from BMC Medical Informatics and Decision Making
#6
of 37 outputs
Altmetric has tracked 22,713,403 research outputs across all sources so far. Compared to these this one has done well and is in the 88th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,982 research outputs from this source. They receive a mean Attention Score of 4.9. This one has done particularly well, scoring higher than 90% 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 199,934 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 88% of its contemporaries.
We're also able to compare this research output to 37 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 83% of its contemporaries.