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Genecentric: a package to uncover graph-theoretic structure in high-throughput epistasis data

Overview of attention for article published in BMC Bioinformatics, January 2013
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (52nd percentile)
  • Average Attention Score compared to outputs of the same age and source

Mentioned by

twitter
3 tweeters

Citations

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4 Dimensions

Readers on

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13 Mendeley
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1 CiteULike
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Title
Genecentric: a package to uncover graph-theoretic structure in high-throughput epistasis data
Published in
BMC Bioinformatics, January 2013
DOI 10.1186/1471-2105-14-23
Pubmed ID
Authors

Andrew Gallant, Mark DM Leiserson, Maxim Kachalov, Lenore J Cowen, Benjamin J Hescott

Abstract

New technology has resulted in high-throughput screens for pairwise genetic interactions in yeast and other model organisms. For each pair in a collection of non-essential genes, an epistasis score is obtained, representing how much sicker (or healthier) the double-knockout organism will be compared to what would be expected from the sickness of the component single knockouts. Recent algorithmic work has identified graph-theoretic patterns in this data that can indicate functional modules, and even sets of genes that may occur in compensatory pathways, such as a BPM-type schema first introduced by Kelley and Ideker. However, to date, any algorithms for finding such patterns in the data were implemented internally, with no software being made publically available.

Twitter Demographics

The data shown below were collected from the profiles of 3 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 13 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 8%
Italy 1 8%
Unknown 11 85%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 5 38%
Researcher 4 31%
Student > Doctoral Student 1 8%
Student > Master 1 8%
Unknown 2 15%
Readers by discipline Count As %
Computer Science 4 31%
Agricultural and Biological Sciences 3 23%
Biochemistry, Genetics and Molecular Biology 2 15%
Business, Management and Accounting 1 8%
Unknown 3 23%

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 January 2013.
All research outputs
#8,355,326
of 14,573,111 outputs
Outputs from BMC Bioinformatics
#3,297
of 5,420 outputs
Outputs of similar age
#115,773
of 246,128 outputs
Outputs of similar age from BMC Bioinformatics
#127
of 192 outputs
Altmetric has tracked 14,573,111 research outputs across all sources so far. This one is in the 42nd percentile – i.e., 42% of other outputs scored the same or lower than it.
So far Altmetric has tracked 5,420 research outputs from this source. They receive a mean Attention Score of 4.9. This one is in the 39th percentile – i.e., 39% 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 246,128 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 52% of its contemporaries.
We're also able to compare this research output to 192 others from the same source and published within six weeks on either side of this one. This one is in the 33rd percentile – i.e., 33% of its contemporaries scored the same or lower than it.