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X Demographics
Mendeley readers
Attention Score in Context
| Title |
MeSH ORA framework: R/Bioconductor packages to support MeSH over-representation analysis
|
|---|---|
| Published in |
BMC Bioinformatics, February 2015
|
| DOI | 10.1186/s12859-015-0453-z |
| Pubmed ID | |
| Authors |
Koki Tsuyuzaki, Gota Morota, Manabu Ishii, Takeru Nakazato, Satoru Miyazaki, Itoshi Nikaido |
| Abstract |
In genome-wide studies, over-representation analysis (ORA) against a set of genes is an essential step for biological interpretation. Many gene annotation resources and software platforms for ORA have been proposed. Recently, Medical Subject Headings (MeSH) terms, which are annotations of PubMed documents, have been used for ORA. MeSH enables the extraction of broader meaning from the gene lists and is expected to become an exhaustive annotation resource for ORA. However, the existing MeSH ORA software platforms are still not sufficient for several reasons. In this work, we developed an original MeSH ORA framework composed of six types of R packages, including MeSH.db, MeSH.AOR.db, MeSH.PCR.db, the org.MeSH.XXX.db-type packages, MeSHDbi, and meshr. Using our framework, users can easily conduct MeSH ORA. By utilizing the enriched MeSH terms, related PubMed documents can be retrieved and saved on local machines within this framework. |
X Demographics
The data shown below were collected from the profiles of 16 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Japan | 2 | 13% |
| Germany | 1 | 6% |
| United States | 1 | 6% |
| Unknown | 12 | 75% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 8 | 50% |
| Scientists | 8 | 50% |
Mendeley readers
The data shown below were compiled from readership statistics for 76 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 76 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 15 | 20% |
| Student > Ph. D. Student | 13 | 17% |
| Student > Doctoral Student | 8 | 11% |
| Student > Bachelor | 7 | 9% |
| Student > Master | 7 | 9% |
| Other | 11 | 14% |
| Unknown | 15 | 20% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 23 | 30% |
| Biochemistry, Genetics and Molecular Biology | 16 | 21% |
| Computer Science | 8 | 11% |
| Veterinary Science and Veterinary Medicine | 5 | 7% |
| Engineering | 2 | 3% |
| Other | 4 | 5% |
| Unknown | 18 | 24% |
Attention Score in Context
This research output has an Altmetric Attention Score of 10. 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 August 2015.
All research outputs
#3,594,549
of 25,728,855 outputs
Outputs from BMC Bioinformatics
#1,138
of 7,738 outputs
Outputs of similar age
#52,869
of 387,200 outputs
Outputs of similar age from BMC Bioinformatics
#15
of 134 outputs
Altmetric has tracked 25,728,855 research outputs across all sources so far. Compared to these this one has done well and is in the 86th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,738 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.6. This one has done well, scoring higher than 85% 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 387,200 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 86% of its contemporaries.
We're also able to compare this research output to 134 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 88% of its contemporaries.