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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Predicting targeted drug combinations based on Pareto optimal patterns of coexpression network connectivity
|
|---|---|
| Published in |
Genome Medicine, April 2014
|
| DOI | 10.1186/gm550 |
| Pubmed ID | |
| Authors |
Nadia M Penrod, Casey S Greene, Jason H Moore |
| Abstract |
Molecularly targeted drugs promise a safer and more effective treatment modality than conventional chemotherapy for cancer patients. However, tumors are dynamic systems that readily adapt to these agents activating alternative survival pathways as they evolve resistant phenotypes. Combination therapies can overcome resistance but finding the optimal combinations efficiently presents a formidable challenge. Here we introduce a new paradigm for the design of combination therapy treatment strategies that exploits the tumor adaptive process to identify context-dependent essential genes as druggable targets. |
X Demographics
The data shown below were collected from the profiles of 16 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 | 9 | 56% |
| Canada | 2 | 13% |
| United Kingdom | 1 | 6% |
| Unknown | 4 | 25% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 9 | 56% |
| Scientists | 7 | 44% |
Mendeley readers
The data shown below were compiled from readership statistics for 33 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 3 | 9% |
| Netherlands | 1 | 3% |
| Unknown | 29 | 88% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 9 | 27% |
| Student > Ph. D. Student | 5 | 15% |
| Other | 4 | 12% |
| Student > Doctoral Student | 3 | 9% |
| Professor > Associate Professor | 3 | 9% |
| Other | 4 | 12% |
| Unknown | 5 | 15% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 10 | 30% |
| Agricultural and Biological Sciences | 6 | 18% |
| Medicine and Dentistry | 3 | 9% |
| Computer Science | 3 | 9% |
| Engineering | 2 | 6% |
| Other | 2 | 6% |
| Unknown | 7 | 21% |
Attention Score in Context
This research output has an Altmetric Attention Score of 11. 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 August 2014.
All research outputs
#3,478,944
of 25,836,587 outputs
Outputs from Genome Medicine
#754
of 1,618 outputs
Outputs of similar age
#33,152
of 242,754 outputs
Outputs of similar age from Genome Medicine
#11
of 23 outputs
Altmetric has tracked 25,836,587 research outputs across all sources so far. Compared to these this one has done well and is in the 86th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,618 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 26.5. This one has gotten more attention than average, scoring higher than 53% 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 242,754 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 86% of its contemporaries.
We're also able to compare this research output to 23 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 52% of its contemporaries.