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Mendeley readers
Attention Score in Context
| Title |
Assessment of network perturbation amplitudes by applying high-throughput data to causal biological networks
|
|---|---|
| Published in |
BMC Systems Biology, May 2012
|
| DOI | 10.1186/1752-0509-6-54 |
| Pubmed ID | |
| Authors |
Florian Martin, Ty M Thomson, Alain Sewer, David A Drubin, Carole Mathis, Dirk Weisensee, Dexter Pratt, Julia Hoeng, Manuel C Peitsch |
| Abstract |
High-throughput measurement technologies produce data sets that have the potential to elucidate the biological impact of disease, drug treatment, and environmental agents on humans. The scientific community faces an ongoing challenge in the analysis of these rich data sources to more accurately characterize biological processes that have been perturbed at the mechanistic level. Here, a new approach is built on previous methodologies in which high-throughput data was interpreted using prior biological knowledge of cause and effect relationships. These relationships are structured into network models that describe specific biological processes, such as inflammatory signaling or cell cycle progression. This enables quantitative assessment of network perturbation in response to a given stimulus. |
Mendeley readers
The data shown below were compiled from readership statistics for 79 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 2 | 3% |
| United Kingdom | 1 | 1% |
| Japan | 1 | 1% |
| Brazil | 1 | 1% |
| Unknown | 74 | 94% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 22 | 28% |
| Student > Ph. D. Student | 19 | 24% |
| Student > Master | 9 | 11% |
| Other | 7 | 9% |
| Professor > Associate Professor | 5 | 6% |
| Other | 12 | 15% |
| Unknown | 5 | 6% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 20 | 25% |
| Biochemistry, Genetics and Molecular Biology | 17 | 22% |
| Computer Science | 10 | 13% |
| Engineering | 7 | 9% |
| Neuroscience | 4 | 5% |
| Other | 14 | 18% |
| Unknown | 7 | 9% |
Attention Score in Context
This research output has an Altmetric Attention Score of 16. 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 21 September 2016.
All research outputs
#1,977,213
of 23,577,654 outputs
Outputs from BMC Systems Biology
#40
of 1,139 outputs
Outputs of similar age
#12,559
of 166,638 outputs
Outputs of similar age from BMC Systems Biology
#2
of 29 outputs
Altmetric has tracked 23,577,654 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 91st percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,139 research outputs from this source. They receive a mean Attention Score of 3.6. 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 166,638 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 92% of its contemporaries.
We're also able to compare this research output to 29 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 93% of its contemporaries.