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Towards human-computer synergetic analysis of large-scale biological data

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

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (92nd percentile)
  • High Attention Score compared to outputs of the same age and source (92nd percentile)

Mentioned by

news
1 news outlet
twitter
14 tweeters

Citations

dimensions_citation
6 Dimensions

Readers on

mendeley
47 Mendeley
citeulike
1 CiteULike
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Title
Towards human-computer synergetic analysis of large-scale biological data
Published in
BMC Bioinformatics, October 2013
DOI 10.1186/1471-2105-14-s14-s10
Pubmed ID
Authors

Rahul Singh, Hui Yang, Ben Dalziel, Daniel Asarnow, William Murad, David Foote, Matthew Gormley, Jonathan Stillman, Susan Fisher

Abstract

Advances in technology have led to the generation of massive amounts of complex and multifarious biological data in areas ranging from genomics to structural biology. The volume and complexity of such data leads to significant challenges in terms of its analysis, especially when one seeks to generate hypotheses or explore the underlying biological processes. At the state-of-the-art, the application of automated algorithms followed by perusal and analysis of the results by an expert continues to be the predominant paradigm for analyzing biological data. This paradigm works well in many problem domains. However, it also is limiting, since domain experts are forced to apply their instincts and expertise such as contextual reasoning, hypothesis formulation, and exploratory analysis after the algorithm has produced its results. In many areas where the organization and interaction of the biological processes is poorly understood and exploratory analysis is crucial, what is needed is to integrate domain expertise during the data analysis process and use it to drive the analysis itself.

Twitter Demographics

The data shown below were collected from the profiles of 14 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 47 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
China 1 2%
Slovenia 1 2%
Brazil 1 2%
Unknown 44 94%

Demographic breakdown

Readers by professional status Count As %
Student > Master 9 19%
Student > Ph. D. Student 8 17%
Student > Bachelor 7 15%
Researcher 6 13%
Student > Doctoral Student 4 9%
Other 9 19%
Unknown 4 9%
Readers by discipline Count As %
Computer Science 9 19%
Agricultural and Biological Sciences 9 19%
Biochemistry, Genetics and Molecular Biology 5 11%
Engineering 4 9%
Business, Management and Accounting 3 6%
Other 9 19%
Unknown 8 17%

Attention Score in Context

This research output has an Altmetric Attention Score of 19. 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 21 February 2016.
All research outputs
#1,501,210
of 20,612,123 outputs
Outputs from BMC Bioinformatics
#358
of 6,803 outputs
Outputs of similar age
#24,371
of 341,501 outputs
Outputs of similar age from BMC Bioinformatics
#30
of 364 outputs
Altmetric has tracked 20,612,123 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 92nd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 6,803 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 particularly well, scoring higher than 94% 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 341,501 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 92% of its contemporaries.
We're also able to compare this research output to 364 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 92% of its contemporaries.