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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Deciphering miRNA transcription factor feed-forward loops to identify drug repurposing candidates for cystic fibrosis
|
|---|---|
| Published in |
Genome Medicine, December 2014
|
| DOI | 10.1186/s13073-014-0094-2 |
| Pubmed ID | |
| Authors |
Zhichao Liu, Jürgen Borlak, Weida Tong |
| Abstract |
Cystic fibrosis (CF) is a fatal genetic disorder caused by mutations in the CF transmembrane conductance regulator (CFTR) gene that primarily affects the lungs and the digestive system, and the current drug treatment is mainly able to alleviate symptoms. To improve disease management for CF, we considered the repurposing of approved drugs and hypothesized that specific microRNA (miRNA) transcription factors (TF) gene networks can be used to generate feed-forward loops (FFLs), thus providing treatment opportunities on the basis of disease specific FFLs. |
X Demographics
The data shown below were collected from the profiles of 5 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 Kingdom | 2 | 40% |
| Canada | 1 | 20% |
| Unknown | 2 | 40% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 3 | 60% |
| Science communicators (journalists, bloggers, editors) | 1 | 20% |
| Scientists | 1 | 20% |
Mendeley readers
The data shown below were compiled from readership statistics for 51 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Brazil | 1 | 2% |
| Unknown | 50 | 98% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 12 | 24% |
| Student > Ph. D. Student | 9 | 18% |
| Student > Doctoral Student | 7 | 14% |
| Professor > Associate Professor | 6 | 12% |
| Student > Bachelor | 4 | 8% |
| Other | 6 | 12% |
| Unknown | 7 | 14% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 15 | 29% |
| Biochemistry, Genetics and Molecular Biology | 6 | 12% |
| Medicine and Dentistry | 6 | 12% |
| Computer Science | 3 | 6% |
| Pharmacology, Toxicology and Pharmaceutical Science | 3 | 6% |
| Other | 8 | 16% |
| Unknown | 10 | 20% |
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 09 December 2014.
All research outputs
#14,178,047
of 24,226,848 outputs
Outputs from Genome Medicine
#1,276
of 1,497 outputs
Outputs of similar age
#183,344
of 370,263 outputs
Outputs of similar age from Genome Medicine
#57
of 69 outputs
Altmetric has tracked 24,226,848 research outputs across all sources so far. This one is in the 40th percentile – i.e., 40% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,497 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 26.6. This one is in the 14th percentile – i.e., 14% 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 370,263 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 49th percentile – i.e., 49% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 69 others from the same source and published within six weeks on either side of this one. This one is in the 17th percentile – i.e., 17% of its contemporaries scored the same or lower than it.