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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Computational analysis and predictive modeling of small molecule modulators of microRNA
|
|---|---|
| Published in |
Journal of Cheminformatics, August 2012
|
| DOI | 10.1186/1758-2946-4-16 |
| Pubmed ID | |
| Authors |
Salma Jamal, Vinita Periwal, OpenSourceDrugDiscovery Consortium, Vinod Scaria |
| Abstract |
MicroRNAs (miRNA) are small endogenously transcribed regulatory RNA which modulates gene expression at a post transcriptional level. These small RNAs have now been shown to be critical regulators in a number of biological processes in the cell including pathophysiology of diseases like cancers. The increasingly evident roles of microRNA in disease processes have also motivated attempts to target them therapeutically. Recently there has been immense interest in understanding small molecule mediated regulation of RNA, including microRNA. |
X Demographics
The data shown below were collected from the profiles of 3 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Germany | 1 | 33% |
| United States | 1 | 33% |
| Italy | 1 | 33% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 2 | 67% |
| Scientists | 1 | 33% |
Mendeley readers
The data shown below were compiled from readership statistics for 48 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| India | 3 | 6% |
| United States | 1 | 2% |
| China | 1 | 2% |
| Belgium | 1 | 2% |
| Unknown | 42 | 88% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 15 | 31% |
| Researcher | 10 | 21% |
| Student > Master | 7 | 15% |
| Student > Postgraduate | 3 | 6% |
| Lecturer > Senior Lecturer | 2 | 4% |
| Other | 5 | 10% |
| Unknown | 6 | 13% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 20 | 42% |
| Chemistry | 7 | 15% |
| Computer Science | 6 | 13% |
| Biochemistry, Genetics and Molecular Biology | 5 | 10% |
| Pharmacology, Toxicology and Pharmaceutical Science | 1 | 2% |
| Other | 2 | 4% |
| Unknown | 7 | 15% |
Attention Score in Context
This research output has an Altmetric Attention Score of 13. 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 28 February 2018.
All research outputs
#2,534,784
of 24,143,470 outputs
Outputs from Journal of Cheminformatics
#240
of 891 outputs
Outputs of similar age
#16,383
of 170,001 outputs
Outputs of similar age from Journal of Cheminformatics
#8
of 12 outputs
Altmetric has tracked 24,143,470 research outputs across all sources so far. Compared to these this one has done well and is in the 89th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 891 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 10.7. This one has gotten more attention than average, scoring higher than 72% 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 170,001 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 90% of its contemporaries.
We're also able to compare this research output to 12 others from the same source and published within six weeks on either side of this one. This one is in the 33rd percentile – i.e., 33% of its contemporaries scored the same or lower than it.