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Rule-based knowledge aggregation for large-scale protein sequence analysis of influenza A viruses

Overview of attention for article published in BMC Bioinformatics, February 2008
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About this Attention Score

  • Good Attention Score compared to outputs of the same age (72nd percentile)
  • Above-average Attention Score compared to outputs of the same age and source (62nd percentile)

Mentioned by

wikipedia
1 Wikipedia page

Citations

dimensions_citation
9 Dimensions

Readers on

mendeley
27 Mendeley
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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
#817,458
of 3,627,879 outputs
Outputs from BMC Bioinformatics
#788
of 2,289 outputs
Outputs of similar age
#24,207
of 93,613 outputs
Outputs of similar age from BMC Bioinformatics
#44
of 127 outputs
Altmetric has tracked 3,627,879 research outputs across all sources so far. This one has received more attention than most of these and is in the 63rd percentile.
So far Altmetric has tracked 2,289 research outputs from this source. They receive a mean Attention Score of 4.3. This one has gotten more attention than average, scoring higher than 58% 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 93,613 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 72% of its contemporaries.
We're also able to compare this research output to 127 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 62% of its contemporaries.