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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Cost sensitive hierarchical document classification to triage PubMed abstracts for manual curation
|
|---|---|
| Published in |
BMC Bioinformatics, December 2011
|
| DOI | 10.1186/1471-2105-12-482 |
| Pubmed ID | |
| Authors |
Emily Seymour, Rohini Damle, Alessandro Sette, Bjoern Peters |
| Abstract |
The Immune Epitope Database (IEDB) project manually curates information from published journal articles that describe immune epitopes derived from a wide variety of organisms and associated with different diseases. In the past, abstracts of scientific articles were retrieved by broad keyword queries of PubMed, and were classified as relevant (curatable) or irrelevant (not curatable) to the scope of the database by a Naïve Bayes classifier. The curatable abstracts were subsequently manually classified into categories corresponding to different disease domains. Over the past four years, we have examined how to further improve this approach in order to enhance classification performance and to reduce the need for manual intervention. |
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 % |
|---|---|---|
| Japan | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 43 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 1 | 2% |
| United States | 1 | 2% |
| Unknown | 41 | 95% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 12 | 28% |
| Student > Master | 8 | 19% |
| Student > Ph. D. Student | 6 | 14% |
| Professor > Associate Professor | 3 | 7% |
| Student > Bachelor | 2 | 5% |
| Other | 7 | 16% |
| Unknown | 5 | 12% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 14 | 33% |
| Agricultural and Biological Sciences | 9 | 21% |
| Medicine and Dentistry | 5 | 12% |
| Chemistry | 2 | 5% |
| Biochemistry, Genetics and Molecular Biology | 1 | 2% |
| Other | 4 | 9% |
| Unknown | 8 | 19% |
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 20 December 2011.
All research outputs
#18,301,870
of 22,659,164 outputs
Outputs from BMC Bioinformatics
#6,278
of 7,240 outputs
Outputs of similar age
#195,490
of 242,888 outputs
Outputs of similar age from BMC Bioinformatics
#81
of 98 outputs
Altmetric has tracked 22,659,164 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 7,240 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 5th percentile – i.e., 5% 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 242,888 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 9th percentile – i.e., 9% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 98 others from the same source and published within six weeks on either side of this one. This one is in the 4th percentile – i.e., 4% of its contemporaries scored the same or lower than it.