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Differential analysis of high-throughput quantitative genetic interaction data

Overview of attention for article published in Genome Biology, December 2012
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Title
Differential analysis of high-throughput quantitative genetic interaction data
Published in
Genome Biology, December 2012
DOI 10.1186/gb-2012-13-12-r123
Pubmed ID
Authors

Gordon J Bean, Trey Ideker

Abstract

Synthetic genetic arrays have been very effective at measuring genetic interactions in yeast in a high-throughput manner and recently have been expanded to measure quantitative changes in interaction, termed 'differential interactions', across multiple conditions. Here, we present a strategy that leverages statistical information from the experimental design to produce a novel, quantitative differential interaction score, which performs favorably compared to previous differential scores. We also discuss the added utility of differential genetic-similarity in differential network analysis. Our approach is preferred for differential network analysis, and our implementation, written in MATLAB, can be found at http://chianti.ucsd.edu/~gbean/compute_differential_scores.m.

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 83 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 4 5%
United Kingdom 3 4%
Sweden 1 1%
Spain 1 1%
Portugal 1 1%
Unknown 73 88%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 27 33%
Researcher 26 31%
Professor > Associate Professor 6 7%
Student > Master 6 7%
Student > Bachelor 5 6%
Other 9 11%
Unknown 4 5%
Readers by discipline Count As %
Agricultural and Biological Sciences 47 57%
Biochemistry, Genetics and Molecular Biology 14 17%
Computer Science 9 11%
Engineering 4 5%
Medicine and Dentistry 3 4%
Other 2 2%
Unknown 4 5%
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 31 December 2012.
All research outputs
#14,387,227
of 25,371,288 outputs
Outputs from Genome Biology
#3,817
of 4,467 outputs
Outputs of similar age
#164,766
of 288,856 outputs
Outputs of similar age from Genome Biology
#39
of 50 outputs
Altmetric has tracked 25,371,288 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 4,467 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 27.6. This one is in the 13th percentile – i.e., 13% 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 288,856 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 42nd percentile – i.e., 42% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 50 others from the same source and published within six weeks on either side of this one. This one is in the 20th percentile – i.e., 20% of its contemporaries scored the same or lower than it.