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Characterizing the state of the art in the computational assignment of gene function: lessons from the first critical assessment of functional annotation (CAFA)

Overview of attention for article published in BMC Bioinformatics, April 2013
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (75th percentile)
  • Good Attention Score compared to outputs of the same age and source (70th percentile)

Mentioned by

twitter
2 tweeters
wikipedia
1 Wikipedia page

Citations

dimensions_citation
27 Dimensions

Readers on

mendeley
46 Mendeley
citeulike
2 CiteULike
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Title
Characterizing the state of the art in the computational assignment of gene function: lessons from the first critical assessment of functional annotation (CAFA)
Published in
BMC Bioinformatics, April 2013
DOI 10.1186/1471-2105-14-s3-s15
Pubmed ID
Authors

Jesse Gillis, Paul Pavlidis

Abstract

The assignment of gene function remains a difficult but important task in computational biology. The establishment of the first Critical Assessment of Functional Annotation (CAFA) was aimed at increasing progress in the field. We present an independent analysis of the results of CAFA, aimed at identifying challenges in assessment and at understanding trends in prediction performance. We found that well-accepted methods based on sequence similarity (i.e., BLAST) have a dominant effect. Many of the most informative predictions turned out to be either recovering existing knowledge about sequence similarity or were "post-dictions" already documented in the literature. These results indicate that deep challenges remain in even defining the task of function assignment, with a particular difficulty posed by the problem of defining function in a way that is not dependent on either flawed gold standards or the input data itself. In particular, we suggest that using the Gene Ontology (or other similar systematizations of function) as a gold standard is unlikely to be the way forward.

Twitter Demographics

The data shown below were collected from the profiles of 2 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 46 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 3 7%
Germany 1 2%
Israel 1 2%
Italy 1 2%
Canada 1 2%
Denmark 1 2%
Spain 1 2%
United States 1 2%
Unknown 36 78%

Demographic breakdown

Readers by professional status Count As %
Student > Master 11 24%
Researcher 11 24%
Student > Ph. D. Student 10 22%
Professor > Associate Professor 4 9%
Professor 3 7%
Other 6 13%
Unknown 1 2%
Readers by discipline Count As %
Agricultural and Biological Sciences 23 50%
Biochemistry, Genetics and Molecular Biology 10 22%
Computer Science 6 13%
Social Sciences 2 4%
Mathematics 1 2%
Other 1 2%
Unknown 3 7%

Attention Score in Context

This research output has an Altmetric Attention Score of 4. 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 16 November 2014.
All research outputs
#853,407
of 4,507,652 outputs
Outputs from BMC Bioinformatics
#750
of 2,646 outputs
Outputs of similar age
#28,038
of 116,465 outputs
Outputs of similar age from BMC Bioinformatics
#38
of 141 outputs
Altmetric has tracked 4,507,652 research outputs across all sources so far. Compared to these this one has done well and is in the 80th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 2,646 research outputs from this source. They receive a mean Attention Score of 4.5. This one has gotten more attention than average, scoring higher than 70% 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 116,465 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 75% of its contemporaries.
We're also able to compare this research output to 141 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 70% of its contemporaries.