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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Categorizer: a tool to categorize genes into user-defined biological groups based on semantic similarity
|
|---|---|
| Published in |
BMC Genomics, December 2014
|
| DOI | 10.1186/1471-2164-15-1091 |
| Pubmed ID | |
| Authors |
Dokyun Na, Hyungbin Son, Jörg Gsponer |
| Abstract |
Communalities between large sets of genes obtained from high-throughput experiments are often identified by searching for enrichments of genes with the same Gene Ontology (GO) annotations. The GO analysis tools used for these enrichment analyses assume that GO terms are independent and the semantic distances between all parent-child terms are identical, which is not true in a biological sense. In addition these tools output lists of often redundant or too specific GO terms, which are difficult to interpret in the context of the biological question investigated by the user. Therefore, there is a demand for a robust and reliable method for gene categorization and enrichment analysis. |
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 States | 1 | 50% |
| Unknown | 1 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 50% |
| Scientists | 1 | 50% |
Mendeley readers
The data shown below were compiled from readership statistics for 69 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Spain | 1 | 1% |
| Germany | 1 | 1% |
| Unknown | 67 | 97% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 18 | 26% |
| Student > Ph. D. Student | 14 | 20% |
| Student > Master | 9 | 13% |
| Student > Postgraduate | 5 | 7% |
| Student > Doctoral Student | 4 | 6% |
| Other | 12 | 17% |
| Unknown | 7 | 10% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 27 | 39% |
| Biochemistry, Genetics and Molecular Biology | 15 | 22% |
| Computer Science | 3 | 4% |
| Medicine and Dentistry | 3 | 4% |
| Immunology and Microbiology | 2 | 3% |
| Other | 5 | 7% |
| Unknown | 14 | 20% |
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 26 February 2016.
All research outputs
#18,387,239
of 22,775,504 outputs
Outputs from BMC Genomics
#8,171
of 10,642 outputs
Outputs of similar age
#261,509
of 361,197 outputs
Outputs of similar age from BMC Genomics
#188
of 241 outputs
Altmetric has tracked 22,775,504 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 10,642 research outputs from this source. They receive a mean Attention Score of 4.7. This one is in the 12th percentile – i.e., 12% 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 361,197 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 16th percentile – i.e., 16% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 241 others from the same source and published within six weeks on either side of this one. This one is in the 10th percentile – i.e., 10% of its contemporaries scored the same or lower than it.