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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Constructing a gene semantic similarity network for the inference of disease genes
|
|---|---|
| Published in |
BMC Systems Biology, December 2011
|
| DOI | 10.1186/1752-0509-5-s2-s2 |
| Pubmed ID | |
| Authors |
Rui Jiang, Mingxin Gan, Peng He |
| Abstract |
The inference of genes that are truly associated with inherited human diseases from a set of candidates resulting from genetic linkage studies has been one of the most challenging tasks in human genetics. Although several computational approaches have been proposed to prioritize candidate genes relying on protein-protein interaction (PPI) networks, these methods can usually cover less than half of known human genes. |
X Demographics
The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 54 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Germany | 1 | 2% |
| Unknown | 53 | 98% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 18 | 33% |
| Researcher | 9 | 17% |
| Professor | 6 | 11% |
| Student > Master | 5 | 9% |
| Student > Doctoral Student | 3 | 6% |
| Other | 9 | 17% |
| Unknown | 4 | 7% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 18 | 33% |
| Agricultural and Biological Sciences | 17 | 31% |
| Biochemistry, Genetics and Molecular Biology | 5 | 9% |
| Physics and Astronomy | 2 | 4% |
| Engineering | 2 | 4% |
| Other | 3 | 6% |
| Unknown | 7 | 13% |
Attention Score in Context
This research output has an Altmetric Attention Score of 7. 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 02 March 2018.
All research outputs
#5,240,751
of 25,374,917 outputs
Outputs from BMC Systems Biology
#141
of 1,132 outputs
Outputs of similar age
#40,753
of 249,142 outputs
Outputs of similar age from BMC Systems Biology
#7
of 36 outputs
Altmetric has tracked 25,374,917 research outputs across all sources so far. Compared to these this one has done well and is in the 79th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,132 research outputs from this source. They receive a mean Attention Score of 3.7. This one has done well, scoring higher than 87% 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 249,142 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 83% of its contemporaries.
We're also able to compare this research output to 36 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 80% of its contemporaries.