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Mendeley readers
Attention Score in Context
| Title |
Rule-based knowledge aggregation for large-scale protein sequence analysis of influenza A viruses
|
|---|---|
| Published in |
BMC Bioinformatics, February 2008
|
| DOI | 10.1186/1471-2105-9-s1-s7 |
| Pubmed ID | |
| Authors |
Olivo Miotto, Tin Wee Tan, Vladimir Brusic |
| Abstract |
The explosive growth of biological data provides opportunities for new statistical and comparative analyses of large information sets, such as alignments comprising tens of thousands of sequences. In such studies, sequence annotations frequently play an essential role, and reliable results depend on metadata quality. However, the semantic heterogeneity and annotation inconsistencies in biological databases greatly increase the complexity of aggregating and cleaning metadata. Manual curation of datasets, traditionally favoured by life scientists, is impractical for studies involving thousands of records. In this study, we investigate quality issues that affect major public databases, and quantify the effectiveness of an automated metadata extraction approach that combines structural and semantic rules. We applied this approach to more than 90,000 influenza A records, to annotate sequences with protein name, virus subtype, isolate, host, geographic origin, and year of isolation. |
Mendeley readers
The data shown below were compiled from readership statistics for 27 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 1 | 4% |
| Netherlands | 1 | 4% |
| Tanzania, United Republic of | 1 | 4% |
| Unknown | 24 | 89% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 5 | 19% |
| Student > Bachelor | 4 | 15% |
| Student > Master | 4 | 15% |
| Student > Ph. D. Student | 4 | 15% |
| Student > Doctoral Student | 1 | 4% |
| Other | 3 | 11% |
| Unknown | 6 | 22% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 7 | 26% |
| Computer Science | 4 | 15% |
| Immunology and Microbiology | 2 | 7% |
| Engineering | 2 | 7% |
| Medicine and Dentistry | 2 | 7% |
| Other | 4 | 15% |
| Unknown | 6 | 22% |
Attention Score in Context
This research output has an Altmetric Attention Score of 3. 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 23 November 2012.
All research outputs
#7,453,350
of 22,786,087 outputs
Outputs from BMC Bioinformatics
#3,023
of 7,279 outputs
Outputs of similar age
#43,321
of 158,136 outputs
Outputs of similar age from BMC Bioinformatics
#15
of 40 outputs
Altmetric has tracked 22,786,087 research outputs across all sources so far. This one is in the 44th percentile – i.e., 44% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,279 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 gotten more attention than average, scoring higher than 50% 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 158,136 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 19th percentile – i.e., 19% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 40 others from the same source and published within six weeks on either side of this one. This one is in the 35th percentile – i.e., 35% of its contemporaries scored the same or lower than it.