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QUADrATiC: scalable gene expression connectivity mapping for repurposing FDA-approved therapeutics

Overview of attention for article published in BMC Bioinformatics, May 2016
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About this Attention Score

  • Good Attention Score compared to outputs of the same age (70th percentile)
  • Good Attention Score compared to outputs of the same age and source (65th percentile)

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8 X users
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Citations

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92 Mendeley
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Title
QUADrATiC: scalable gene expression connectivity mapping for repurposing FDA-approved therapeutics
Published in
BMC Bioinformatics, May 2016
DOI 10.1186/s12859-016-1062-1
Pubmed ID
Authors

Paul G. O’Reilly, Qing Wen, Peter Bankhead, Philip D. Dunne, Darragh G. McArt, Suzanne McPherson, Peter W. Hamilton, Ken I. Mills, Shu-Dong Zhang

Abstract

Gene expression connectivity mapping has proven to be a powerful and flexible tool for research. Its application has been shown in a broad range of research topics, most commonly as a means of identifying potential small molecule compounds, which may be further investigated as candidates for repurposing to treat diseases. The public release of voluminous data from the Library of Integrated Cellular Signatures (LINCS) programme further enhanced the utilities and potentials of gene expression connectivity mapping in biomedicine. We describe QUADrATiC ( http://go.qub.ac.uk/QUADrATiC ), a user-friendly tool for the exploration of gene expression connectivity on the subset of the LINCS data set corresponding to FDA-approved small molecule compounds. It enables the identification of compounds for repurposing therapeutic potentials. The software is designed to cope with the increased volume of data over existing tools, by taking advantage of multicore computing architectures to provide a scalable solution, which may be installed and operated on a range of computers, from laptops to servers. This scalability is provided by the use of the modern concurrent programming paradigm provided by the Akka framework. The QUADrATiC Graphical User Interface (GUI) has been developed using advanced Javascript frameworks, providing novel visualization capabilities for further analysis of connections. There is also a web services interface, allowing integration with other programs or scripts. QUADrATiC has been shown to provide an improvement over existing connectivity map software, in terms of scope (based on the LINCS data set), applicability (using FDA-approved compounds), usability and speed. It offers potential to biological researchers to analyze transcriptional data and generate potential therapeutics for focussed study in the lab. QUADrATiC represents a step change in the process of investigating gene expression connectivity and provides more biologically-relevant results than previous alternative solutions.

X Demographics

X Demographics

The data shown below were collected from the profiles of 8 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 92 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Cuba 1 1%
Hungary 1 1%
Netherlands 1 1%
Russia 1 1%
Unknown 88 96%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 20 22%
Researcher 18 20%
Student > Bachelor 10 11%
Student > Doctoral Student 6 7%
Other 5 5%
Other 21 23%
Unknown 12 13%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 15 16%
Computer Science 14 15%
Agricultural and Biological Sciences 11 12%
Medicine and Dentistry 11 12%
Engineering 7 8%
Other 19 21%
Unknown 15 16%
Attention Score in Context

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 22 March 2017.
All research outputs
#6,119,286
of 22,867,327 outputs
Outputs from BMC Bioinformatics
#2,298
of 7,295 outputs
Outputs of similar age
#86,557
of 298,972 outputs
Outputs of similar age from BMC Bioinformatics
#35
of 102 outputs
Altmetric has tracked 22,867,327 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 7,295 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 298,972 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 70% of its contemporaries.
We're also able to compare this research output to 102 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 65% of its contemporaries.