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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Influence networks based on coexpression improve drug target discovery for the development of novel cancer therapeutics
|
|---|---|
| Published in |
BMC Systems Biology, February 2014
|
| DOI | 10.1186/1752-0509-8-12 |
| Pubmed ID | |
| Authors |
Nadia M Penrod, Jason H Moore |
| Abstract |
The demand for novel molecularly targeted drugs will continue to rise as we move forward toward the goal of personalizing cancer treatment to the molecular signature of individual tumors. However, the identification of targets and combinations of targets that can be safely and effectively modulated is one of the greatest challenges facing the drug discovery process. A promising approach is to use biological networks to prioritize targets based on their relative positions to one another, a property that affects their ability to maintain network integrity and propagate information-flow. Here, we introduce influence networks and demonstrate how they can be used to generate influence scores as a network-based metric to rank genes as potential drug targets. |
X Demographics
The data shown below were collected from the profiles of 11 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 | 36% |
| United Kingdom | 1 | 9% |
| Unknown | 6 | 55% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 6 | 55% |
| Scientists | 4 | 36% |
| Practitioners (doctors, other healthcare professionals) | 1 | 9% |
Mendeley readers
The data shown below were compiled from readership statistics for 41 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 4 | 10% |
| Unknown | 37 | 90% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 12 | 29% |
| Student > Ph. D. Student | 11 | 27% |
| Student > Doctoral Student | 4 | 10% |
| Professor | 4 | 10% |
| Professor > Associate Professor | 3 | 7% |
| Other | 5 | 12% |
| Unknown | 2 | 5% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 16 | 39% |
| Computer Science | 8 | 20% |
| Biochemistry, Genetics and Molecular Biology | 4 | 10% |
| Medicine and Dentistry | 2 | 5% |
| Engineering | 2 | 5% |
| Other | 4 | 10% |
| Unknown | 5 | 12% |
Attention Score in Context
This research output has an Altmetric Attention Score of 6. 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 22 January 2015.
All research outputs
#6,346,157
of 25,706,302 outputs
Outputs from BMC Systems Biology
#171
of 1,132 outputs
Outputs of similar age
#68,274
of 324,391 outputs
Outputs of similar age from BMC Systems Biology
#5
of 36 outputs
Altmetric has tracked 25,706,302 research outputs across all sources so far. Compared to these this one has done well and is in the 75th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,132 research outputs from this source. They receive a mean Attention Score of 3.7. This one has done well, scoring higher than 84% 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 324,391 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 78% of its contemporaries.
We're also able to compare this research output to 36 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 86% of its contemporaries.