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X Demographics
Mendeley readers
Attention Score in Context
| Title |
EnRICH: Extraction and Ranking using Integration and Criteria Heuristics
|
|---|---|
| Published in |
BMC Systems Biology, January 2013
|
| DOI | 10.1186/1752-0509-7-4 |
| Pubmed ID | |
| Authors |
Xia Zhang, M Heather West Greenlee, Jeanne M Serb |
| Abstract |
High throughput screening technologies enable biologists to generate candidate genes at a rate that, due to time and cost constraints, cannot be studied by experimental approaches in the laboratory. Thus, it has become increasingly important to prioritize candidate genes for experiments. To accomplish this, researchers need to apply selection requirements based on their knowledge, which necessitates qualitative integration of heterogeneous data sources and filtration using multiple criteria. A similar approach can also be applied to putative candidate gene relationships. While automation can assist in this routine and imperative procedure, flexibility of data sources and criteria must not be sacrificed. A tool that can optimize the trade-off between automation and flexibility to simultaneously filter and qualitatively integrate data is needed to prioritize candidate genes and generate composite networks from heterogeneous data sources. |
X Demographics
The data shown below were collected from the profiles of 2 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 Kingdom | 1 | 50% |
| Unknown | 1 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 1 | 50% |
| Practitioners (doctors, other healthcare professionals) | 1 | 50% |
Mendeley readers
The data shown below were compiled from readership statistics for 24 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Germany | 1 | 4% |
| Unknown | 23 | 96% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 5 | 21% |
| Student > Doctoral Student | 3 | 13% |
| Student > Bachelor | 3 | 13% |
| Professor | 3 | 13% |
| Student > Ph. D. Student | 2 | 8% |
| Other | 6 | 25% |
| Unknown | 2 | 8% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 8 | 33% |
| Biochemistry, Genetics and Molecular Biology | 4 | 17% |
| Medicine and Dentistry | 3 | 13% |
| Engineering | 3 | 13% |
| Computer Science | 2 | 8% |
| Other | 2 | 8% |
| Unknown | 2 | 8% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. 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 30 January 2013.
All research outputs
#18,616,159
of 23,881,329 outputs
Outputs from BMC Systems Biology
#771
of 1,126 outputs
Outputs of similar age
#235,267
of 312,063 outputs
Outputs of similar age from BMC Systems Biology
#37
of 56 outputs
Altmetric has tracked 23,881,329 research outputs across all sources so far. This one is in the 19th percentile – i.e., 19% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,126 research outputs from this source. They receive a mean Attention Score of 3.6. This one is in the 27th percentile – i.e., 27% 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 312,063 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 21st percentile – i.e., 21% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 56 others from the same source and published within six weeks on either side of this one. This one is in the 32nd percentile – i.e., 32% of its contemporaries scored the same or lower than it.