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Efficient discovery of responses of proteins to compounds using active learning

Overview of attention for article published in BMC Bioinformatics, May 2014
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About this Attention Score

  • Good Attention Score compared to outputs of the same age (74th percentile)
  • High Attention Score compared to outputs of the same age and source (88th percentile)

Mentioned by

twitter
11 tweeters

Citations

dimensions_citation
29 Dimensions

Readers on

mendeley
85 Mendeley
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Title
Efficient discovery of responses of proteins to compounds using active learning
Published in
BMC Bioinformatics, May 2014
DOI 10.1186/1471-2105-15-143
Pubmed ID
Authors

Joshua D Kangas, Armaghan W Naik, Robert F Murphy

Abstract

Drug discovery and development has been aided by high throughput screening methods that detect compound effects on a single target. However, when using focused initial screening, undesirable secondary effects are often detected late in the development process after significant investment has been made. An alternative approach would be to screen against undesired effects early in the process, but the number of possible secondary targets makes this prohibitively expensive.

Twitter Demographics

The data shown below were collected from the profiles of 11 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 85 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 4 5%
Russia 1 1%
Unknown 80 94%

Demographic breakdown

Readers by professional status Count As %
Researcher 21 25%
Student > Ph. D. Student 19 22%
Student > Master 9 11%
Student > Doctoral Student 6 7%
Student > Bachelor 6 7%
Other 13 15%
Unknown 11 13%
Readers by discipline Count As %
Agricultural and Biological Sciences 15 18%
Computer Science 13 15%
Biochemistry, Genetics and Molecular Biology 9 11%
Chemistry 9 11%
Medicine and Dentistry 7 8%
Other 21 25%
Unknown 11 13%

Attention Score in Context

This research output has an Altmetric Attention Score of 5. 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 08 September 2020.
All research outputs
#5,662,293
of 21,353,399 outputs
Outputs from BMC Bioinformatics
#2,184
of 6,933 outputs
Outputs of similar age
#52,424
of 204,676 outputs
Outputs of similar age from BMC Bioinformatics
#3
of 18 outputs
Altmetric has tracked 21,353,399 research outputs across all sources so far. This one has received more attention than most of these and is in the 73rd percentile.
So far Altmetric has tracked 6,933 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 68% 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 204,676 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 74% of its contemporaries.
We're also able to compare this research output to 18 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 88% of its contemporaries.