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Combining guilt-by-association and guilt-by-profiling to predict Saccharomyces cerevisiae gene function

Overview of attention for article published in Genome Biology (Online Edition), January 2008
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1 tweeter

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97 Mendeley
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Title
Combining guilt-by-association and guilt-by-profiling to predict Saccharomyces cerevisiae gene function
Published in
Genome Biology (Online Edition), January 2008
DOI 10.1186/gb-2008-9-s1-s7
Pubmed ID
Authors

Weidong Tian, Lan V Zhang, Murat Taşan, Francis D Gibbons, Oliver D King, Julie Park, Zeba Wunderlich, J Michael Cherry, Frederick P Roth

Abstract

Learning the function of genes is a major goal of computational genomics. Methods for inferring gene function have typically fallen into two categories: 'guilt-by-profiling', which exploits correlation between function and other gene characteristics; and 'guilt-by-association', which transfers function from one gene to another via biological relationships.

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

Geographical breakdown

Country Count As %
United Kingdom 4 4%
United States 3 3%
Canada 2 2%
Korea, Republic of 1 1%
Slovenia 1 1%
Belgium 1 1%
Spain 1 1%
Germany 1 1%
Unknown 83 86%

Demographic breakdown

Readers by professional status Count As %
Researcher 31 32%
Student > Ph. D. Student 29 30%
Student > Bachelor 8 8%
Professor 7 7%
Student > Master 6 6%
Other 11 11%
Unknown 5 5%
Readers by discipline Count As %
Agricultural and Biological Sciences 45 46%
Biochemistry, Genetics and Molecular Biology 16 16%
Computer Science 15 15%
Medicine and Dentistry 7 7%
Engineering 4 4%
Other 6 6%
Unknown 4 4%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 04 April 2012.
All research outputs
#3,082,871
of 4,506,977 outputs
Outputs from Genome Biology (Online Edition)
#1,685
of 1,809 outputs
Outputs of similar age
#47,163
of 76,514 outputs
Outputs of similar age from Genome Biology (Online Edition)
#48
of 54 outputs
Altmetric has tracked 4,506,977 research outputs across all sources so far. This one is in the 21st percentile – i.e., 21% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,809 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 15.1. This one is in the 5th percentile – i.e., 5% 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 76,514 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 21st percentile – i.e., 21% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 54 others from the same source and published within six weeks on either side of this one. This one is in the 7th percentile – i.e., 7% of its contemporaries scored the same or lower than it.