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TOPS: a versatile software tool for statistical analysis and visualization of combinatorial gene-gene and gene-drug interaction screens

Overview of attention for article published in BMC Bioinformatics, April 2014
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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 (85th percentile)
  • High Attention Score compared to outputs of the same age and source (81st percentile)

Mentioned by

blogs
1 blog
twitter
6 X users

Citations

dimensions_citation
6 Dimensions

Readers on

mendeley
55 Mendeley
citeulike
4 CiteULike
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Title
TOPS: a versatile software tool for statistical analysis and visualization of combinatorial gene-gene and gene-drug interaction screens
Published in
BMC Bioinformatics, April 2014
DOI 10.1186/1471-2105-15-98
Pubmed ID
Authors

Markus K Muellner, Gerhard Duernberger, Florian Ganglberger, Claudia Kerzendorfer, Iris Z Uras, Andreas Schoenegger, Klaudia Bagienski, Jacques Colinge, Sebastian MB Nijman

Abstract

Measuring the impact of combinations of genetic or chemical perturbations on cellular fitness, sometimes referred to as synthetic lethal screening, is a powerful method for obtaining novel insights into gene function and drug action. Especially when performed at large scales, gene-gene or gene-drug interaction screens can reveal complex genetic interactions or drug mechanism of action or even identify novel therapeutics for the treatment of diseases.The result of such large-scale screen results can be represented as a matrix with a numeric score indicating the cellular fitness (e.g. viability or doubling time) for each double perturbation. In a typical screen, the majority of combinations do not impact the cellular fitness. Thus, it is critical to first discern true "hits" from noise. Subsequent data exploration and visualization methods can assist to extract meaningful biological information from the data. However, despite the increasing interest in combination perturbation screens, no user friendly open-source program exists that combines statistical analysis, data exploration tools and visualization.

X Demographics

X Demographics

The data shown below were collected from the profiles of 6 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 55 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 2%
United States 1 2%
Germany 1 2%
Brazil 1 2%
Unknown 51 93%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 12 22%
Researcher 11 20%
Student > Doctoral Student 5 9%
Student > Master 3 5%
Professor 3 5%
Other 9 16%
Unknown 12 22%
Readers by discipline Count As %
Agricultural and Biological Sciences 16 29%
Biochemistry, Genetics and Molecular Biology 9 16%
Computer Science 4 7%
Medicine and Dentistry 3 5%
Arts and Humanities 2 4%
Other 9 16%
Unknown 12 22%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 10. 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 26 September 2014.
All research outputs
#3,188,524
of 22,753,345 outputs
Outputs from BMC Bioinformatics
#1,175
of 7,269 outputs
Outputs of similar age
#33,820
of 228,038 outputs
Outputs of similar age from BMC Bioinformatics
#22
of 118 outputs
Altmetric has tracked 22,753,345 research outputs across all sources so far. Compared to these this one has done well and is in the 85th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,269 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 well, scoring higher than 83% 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 228,038 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 85% of its contemporaries.
We're also able to compare this research output to 118 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 81% of its contemporaries.