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A phenome-guided drug repositioning through a latent variable model

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

  • Above-average Attention Score compared to outputs of the same age (51st percentile)
  • Average Attention Score compared to outputs of the same age and source

Mentioned by

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5 X users
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1 Facebook page

Citations

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40 Dimensions

Readers on

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63 Mendeley
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Title
A phenome-guided drug repositioning through a latent variable model
Published in
BMC Bioinformatics, August 2014
DOI 10.1186/1471-2105-15-267
Pubmed ID
Authors

Halil Bisgin, Zhichao Liu, Hong Fang, Reagan Kelly, Xiaowei Xu, Weida Tong

Abstract

The phenome represents a distinct set of information in the human population. It has been explored particularly in its relationship with the genome to identify correlations for diseases. The phenome has been also explored for drug repositioning with efforts focusing on the search space for the most similar candidate drugs. For a comprehensive analysis of the phenome, we assumed that all phenotypes (indications and side effects) were inter-connected with a probabilistic distribution and this characteristic may offer an opportunity to identify new therapeutic indications for a given drug. Correspondingly, we employed Latent Dirichlet Allocation (LDA), which introduces latent variables (topics) to govern the phenome distribution.

X Demographics

X Demographics

The data shown below were collected from the profiles of 5 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 63 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 3%
France 1 2%
Germany 1 2%
Unknown 59 94%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 15 24%
Researcher 11 17%
Student > Master 9 14%
Other 7 11%
Student > Bachelor 6 10%
Other 8 13%
Unknown 7 11%
Readers by discipline Count As %
Agricultural and Biological Sciences 15 24%
Computer Science 12 19%
Biochemistry, Genetics and Molecular Biology 7 11%
Medicine and Dentistry 5 8%
Chemistry 3 5%
Other 10 16%
Unknown 11 17%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 18 August 2014.
All research outputs
#14,890,014
of 25,540,105 outputs
Outputs from BMC Bioinformatics
#4,252
of 7,717 outputs
Outputs of similar age
#116,849
of 242,333 outputs
Outputs of similar age from BMC Bioinformatics
#72
of 122 outputs
Altmetric has tracked 25,540,105 research outputs across all sources so far. This one is in the 41st percentile – i.e., 41% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,717 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.5. This one is in the 44th percentile – i.e., 44% of its peers scored the same or lower than it.
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 242,333 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 51% of its contemporaries.
We're also able to compare this research output to 122 others from the same source and published within six weeks on either side of this one. This one is in the 40th percentile – i.e., 40% of its contemporaries scored the same or lower than it.