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Attention Score in Context
| Title |
A random set scoring model for prioritization of disease candidate genes using protein complexes and data-mining of GeneRIF, OMIM and PubMed records
|
|---|---|
| Published in |
BMC Bioinformatics, September 2014
|
| DOI | 10.1186/1471-2105-15-315 |
| Pubmed ID | |
| Authors |
Li Jiang, Stefan M Edwards, Bo Thomsen, Christopher T Workman, Bernt Guldbrandtsen, Peter Sørensen |
| Abstract |
Prioritizing genetic variants is a challenge because disease susceptibility loci are often located in genes of unknown function or the relationship with the corresponding phenotype is unclear. A global data-mining exercise on the biomedical literature can establish the phenotypic profile of genes with respect to their connection to disease phenotypes. The importance of protein-protein interaction networks in the genetic heterogeneity of common diseases or complex traits is becoming increasingly recognized. Thus, the development of a network-based approach combined with phenotypic profiling would be useful for disease gene prioritization. |
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 % |
|---|---|---|
| Canada | 1 | 33% |
| United States | 1 | 33% |
| Unknown | 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 56 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Korea, Republic of | 1 | 2% |
| Spain | 1 | 2% |
| Netherlands | 1 | 2% |
| Denmark | 1 | 2% |
| Unknown | 52 | 93% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 13 | 23% |
| Student > Ph. D. Student | 12 | 21% |
| Student > Bachelor | 7 | 13% |
| Professor > Associate Professor | 5 | 9% |
| Student > Master | 5 | 9% |
| Other | 5 | 9% |
| Unknown | 9 | 16% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 13 | 23% |
| Computer Science | 12 | 21% |
| Medicine and Dentistry | 9 | 16% |
| Biochemistry, Genetics and Molecular Biology | 7 | 13% |
| Mathematics | 1 | 2% |
| Other | 4 | 7% |
| Unknown | 10 | 18% |
Attention Score in Context
This research output has an Altmetric Attention Score of 2. 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 19 November 2014.
All research outputs
#13,413,381
of 22,764,165 outputs
Outputs from BMC Bioinformatics
#4,192
of 7,273 outputs
Outputs of similar age
#120,127
of 252,171 outputs
Outputs of similar age from BMC Bioinformatics
#62
of 112 outputs
Altmetric has tracked 22,764,165 research outputs across all sources so far. This one is in the 39th percentile – i.e., 39% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,273 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one is in the 38th percentile – i.e., 38% 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 252,171 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 50% of its contemporaries.
We're also able to compare this research output to 112 others from the same source and published within six weeks on either side of this one. This one is in the 42nd percentile – i.e., 42% of its contemporaries scored the same or lower than it.