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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Identifying biological concepts from a protein-related corpus with a probabilistic topic model
|
|---|---|
| Published in |
BMC Bioinformatics, February 2006
|
| DOI | 10.1186/1471-2105-7-58 |
| Pubmed ID | |
| Authors |
Bin Zheng, David C McLean, Xinghua Lu |
| Abstract |
Biomedical literature, e.g., MEDLINE, contains a wealth of knowledge regarding functions of proteins. Major recurring biological concepts within such text corpora represent the domains of this body of knowledge. The goal of this research is to identify the major biological topics/concepts from a corpus of protein-related MEDLINE titles and abstracts by applying a probabilistic topic model. |
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 % |
|---|---|---|
| Ireland | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 49 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% |
| Unknown | 47 | 96% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 11 | 22% |
| Student > Master | 9 | 18% |
| Researcher | 6 | 12% |
| Student > Doctoral Student | 4 | 8% |
| Student > Postgraduate | 3 | 6% |
| Other | 8 | 16% |
| Unknown | 8 | 16% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 16 | 33% |
| Agricultural and Biological Sciences | 7 | 14% |
| Business, Management and Accounting | 3 | 6% |
| Social Sciences | 3 | 6% |
| Medicine and Dentistry | 2 | 4% |
| Other | 5 | 10% |
| Unknown | 13 | 27% |
Attention Score in Context
This research output has an Altmetric Attention Score of 11. 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 05 November 2020.
All research outputs
#2,694,440
of 22,743,667 outputs
Outputs from BMC Bioinformatics
#890
of 7,268 outputs
Outputs of similar age
#9,626
of 154,817 outputs
Outputs of similar age from BMC Bioinformatics
#6
of 54 outputs
Altmetric has tracked 22,743,667 research outputs across all sources so far. Compared to these this one has done well and is in the 87th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,268 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one has done well, scoring higher than 87% 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 154,817 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 93% of its contemporaries.
We're also able to compare this research output to 54 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 87% of its contemporaries.