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Using protein complexes to predict phenotypic effects of gene mutation

Overview of attention for article published in Genome Biology (Online Edition), January 2007
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About this Attention Score

  • Good Attention Score compared to outputs of the same age (65th percentile)

Mentioned by

wikipedia
1 Wikipedia page

Citations

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81 Dimensions

Readers on

mendeley
82 Mendeley
citeulike
12 CiteULike
connotea
3 Connotea
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Title
Using protein complexes to predict phenotypic effects of gene mutation
Published in
Genome Biology (Online Edition), January 2007
DOI 10.1186/gb-2007-8-11-r252
Pubmed ID
Authors

Hunter B Fraser, Joshua B Plotkin

Abstract

Predicting the phenotypic effects of mutations is a central goal of genetics research; it has important applications in elucidating how genotype determines phenotype and in identifying human disease genes. Using a wide range of functional genomic data from the yeast Saccharomyces cerevisiae, we show that the best predictor of a protein's knockout phenotype is the knockout phenotype of other proteins that are present in a protein complex with it. Even the addition of multiple datasets does not improve upon the predictions made from protein complex membership. Similarly, we find that a proxy for protein complexes is a powerful predictor of disease phenotypes in humans. We propose that identifying human protein complexes containing known disease genes will be an efficient method for large-scale disease gene discovery, and that yeast may prove to be an informative model system for investigating, and even predicting, the genetic basis of both Mendelian and complex disease phenotypes.

Mendeley readers

The data shown below were compiled from readership statistics for 82 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 3 4%
United Kingdom 3 4%
United States 2 2%
India 1 1%
Finland 1 1%
Australia 1 1%
Poland 1 1%
Unknown 70 85%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 30 37%
Researcher 23 28%
Student > Master 7 9%
Professor 5 6%
Professor > Associate Professor 5 6%
Other 11 13%
Unknown 1 1%
Readers by discipline Count As %
Agricultural and Biological Sciences 59 72%
Biochemistry, Genetics and Molecular Biology 10 12%
Computer Science 8 10%
Medicine and Dentistry 2 2%
Chemistry 1 1%
Other 1 1%
Unknown 1 1%

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 21 February 2014.
All research outputs
#3,624,137
of 12,486,858 outputs
Outputs from Genome Biology (Online Edition)
#2,054
of 2,835 outputs
Outputs of similar age
#72,031
of 234,629 outputs
Outputs of similar age from Genome Biology (Online Edition)
#11
of 18 outputs
Altmetric has tracked 12,486,858 research outputs across all sources so far. This one is in the 49th percentile – i.e., 49% of other outputs scored the same or lower than it.
So far Altmetric has tracked 2,835 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 22.9. This one is in the 18th percentile – i.e., 18% 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 234,629 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 65% of its contemporaries.
We're also able to compare this research output to 18 others from the same source and published within six weeks on either side of this one. This one is in the 22nd percentile – i.e., 22% of its contemporaries scored the same or lower than it.