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The PathOlogist: an automated tool for pathway-centric analysis

Overview of attention for article published in BMC Bioinformatics, May 2011
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About this Attention Score

  • Good Attention Score compared to outputs of the same age (69th percentile)
  • Average Attention Score compared to outputs of the same age and source

Mentioned by

twitter
1 tweeter
patent
1 patent

Citations

dimensions_citation
27 Dimensions

Readers on

mendeley
95 Mendeley
citeulike
9 CiteULike
connotea
1 Connotea

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Summary Twitter Patents Dimensions citations
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Title
The PathOlogist: an automated tool for pathway-centric analysis
Published in
BMC Bioinformatics, May 2011
DOI 10.1186/1471-2105-12-133
Pubmed ID
21542931
Authors

Sharon I Greenblum, Sol Efroni, Carl F Schaefer, Ken H Buetow

Abstract

The PathOlogist is a new tool designed to transform large sets of gene expression data into quantitative descriptors of pathway-level behavior. The tool aims to provide a robust alternative to the search for single-gene-to-phenotype associations by accounting for the complexity of molecular interactions.

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Twitter Demographics

The data shown below were collected from the profile of 1 tweeter 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 95 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
France 2 2%
United States 2 2%
Belgium 2 2%
Mexico 1 1%
United Kingdom 1 1%
Denmark 1 1%
Russia 1 1%
India 1 1%
Spain 1 1%
Other 0 0%
Unknown 83 87%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 29 31%
Researcher 24 25%
Student > Master 11 12%
Professor > Associate Professor 7 7%
Other 5 5%
Other 15 16%
Unknown 4 4%
Readers by discipline Count As %
Agricultural and Biological Sciences 41 43%
Computer Science 13 14%
Biochemistry, Genetics and Molecular Biology 12 13%
Medicine and Dentistry 11 12%
Mathematics 3 3%
Other 6 6%
Unknown 9 9%

Attention Score in Context

This research output has an Altmetric Attention Score of 4. 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 20 February 2014.
All research outputs
#3,344,684
of 12,373,386 outputs
Outputs from BMC Bioinformatics
#1,536
of 4,576 outputs
Outputs of similar age
#25,468
of 85,810 outputs
Outputs of similar age from BMC Bioinformatics
#17
of 32 outputs
Altmetric has tracked 12,373,386 research outputs across all sources so far. This one has received more attention than most of these and is in the 72nd percentile.
So far Altmetric has tracked 4,576 research outputs from this source. They receive a mean Attention Score of 4.9. This one has gotten more attention than average, scoring higher than 65% 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 85,810 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 69% of its contemporaries.
We're also able to compare this research output to 32 others from the same source and published within six weeks on either side of this one. This one is in the 46th percentile – i.e., 46% of its contemporaries scored the same or lower than it.

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