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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Prediction of Drug-Target Interactions and Drug Repositioning via Network-Based Inference
|
|---|---|
| Published in |
PLoS Computational Biology, May 2012
|
| DOI | 10.1371/journal.pcbi.1002503 |
| Pubmed ID | |
| Authors |
Feixiong Cheng, Chuang Liu, Jing Jiang, Weiqiang Lu, Weihua Li, Guixia Liu, Weixing Zhou, Jin Huang, Yun Tang |
X Demographics
The data shown below were collected from the profiles of 8 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 | 3 | 38% |
| Austria | 1 | 13% |
| South Africa | 1 | 13% |
| Canada | 1 | 13% |
| United Kingdom | 1 | 13% |
| Unknown | 1 | 13% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 4 | 50% |
| Members of the public | 4 | 50% |
Mendeley readers
The data shown below were compiled from readership statistics for 652 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 15 | 2% |
| United Kingdom | 6 | <1% |
| Spain | 5 | <1% |
| Brazil | 3 | <1% |
| China | 3 | <1% |
| India | 3 | <1% |
| Canada | 2 | <1% |
| Germany | 2 | <1% |
| New Caledonia | 1 | <1% |
| Other | 7 | 1% |
| Unknown | 605 | 93% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 171 | 26% |
| Researcher | 117 | 18% |
| Student > Master | 86 | 13% |
| Student > Bachelor | 42 | 6% |
| Professor > Associate Professor | 36 | 6% |
| Other | 113 | 17% |
| Unknown | 87 | 13% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 153 | 23% |
| Computer Science | 120 | 18% |
| Biochemistry, Genetics and Molecular Biology | 77 | 12% |
| Chemistry | 46 | 7% |
| Medicine and Dentistry | 34 | 5% |
| Other | 108 | 17% |
| Unknown | 114 | 17% |
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 23 October 2017.
All research outputs
#6,537,141
of 25,837,817 outputs
Outputs from PLoS Computational Biology
#4,432
of 9,027 outputs
Outputs of similar age
#42,993
of 178,099 outputs
Outputs of similar age from PLoS Computational Biology
#42
of 105 outputs
Altmetric has tracked 25,837,817 research outputs across all sources so far. This one has received more attention than most of these and is in the 74th percentile.
So far Altmetric has tracked 9,027 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 20.4. This one has gotten more attention than average, scoring higher than 50% 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 178,099 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 75% of its contemporaries.
We're also able to compare this research output to 105 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 60% of its contemporaries.