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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Chem2Bio2RDF: a semantic framework for linking and data mining chemogenomic and systems chemical biology data
|
|---|---|
| Published in |
BMC Bioinformatics, May 2010
|
| DOI | 10.1186/1471-2105-11-255 |
| Pubmed ID | |
| Authors |
Bin Chen, Xiao Dong, Dazhi Jiao, Huijun Wang, Qian Zhu, Ying Ding, David J Wild |
| Abstract |
Recently there has been an explosion of new data sources about genes, proteins, genetic variations, chemical compounds, diseases and drugs. Integration of these data sources and the identification of patterns that go across them is of critical interest. Initiatives such as Bio2RDF and LODD have tackled the problem of linking biological data and drug data respectively using RDF. Thus far, the inclusion of chemogenomic and systems chemical biology information that crosses the domains of chemistry and biology has been very limited |
X Demographics
The data shown below were collected from the profiles of 4 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 | 1 | 25% |
| Unknown | 3 | 75% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 3 | 75% |
| Scientists | 1 | 25% |
Mendeley readers
The data shown below were compiled from readership statistics for 218 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 6 | 3% |
| United Kingdom | 5 | 2% |
| Netherlands | 3 | 1% |
| Germany | 3 | 1% |
| Canada | 2 | <1% |
| Korea, Republic of | 1 | <1% |
| Italy | 1 | <1% |
| Portugal | 1 | <1% |
| India | 1 | <1% |
| Other | 6 | 3% |
| Unknown | 189 | 87% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 54 | 25% |
| Researcher | 52 | 24% |
| Student > Master | 27 | 12% |
| Professor > Associate Professor | 16 | 7% |
| Other | 13 | 6% |
| Other | 35 | 16% |
| Unknown | 21 | 10% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 68 | 31% |
| Agricultural and Biological Sciences | 48 | 22% |
| Chemistry | 27 | 12% |
| Biochemistry, Genetics and Molecular Biology | 13 | 6% |
| Medicine and Dentistry | 9 | 4% |
| Other | 24 | 11% |
| Unknown | 29 | 13% |
Attention Score in Context
This research output has an Altmetric Attention Score of 4. 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 11 September 2013.
All research outputs
#8,057,120
of 24,903,209 outputs
Outputs from BMC Bioinformatics
#3,015
of 7,606 outputs
Outputs of similar age
#35,504
of 100,277 outputs
Outputs of similar age from BMC Bioinformatics
#33
of 71 outputs
Altmetric has tracked 24,903,209 research outputs across all sources so far. This one has received more attention than most of these and is in the 67th percentile.
So far Altmetric has tracked 7,606 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.5. This one has gotten more attention than average, scoring higher than 58% 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 100,277 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 63% of its contemporaries.
We're also able to compare this research output to 71 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.