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An algorithm for chemical genomic profiling that minimizes batch effects: bucket evaluations

Overview of attention for article published in BMC Bioinformatics, September 2012
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About this Attention Score

  • Good Attention Score compared to outputs of the same age (71st percentile)
  • Good Attention Score compared to outputs of the same age and source (65th percentile)

Mentioned by

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2 X users
wikipedia
1 Wikipedia page

Citations

dimensions_citation
2 Dimensions

Readers on

mendeley
17 Mendeley
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1 CiteULike
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Title
An algorithm for chemical genomic profiling that minimizes batch effects: bucket evaluations
Published in
BMC Bioinformatics, September 2012
DOI 10.1186/1471-2105-13-245
Pubmed ID
Authors

Daniel Shabtai, Guri Giaever, Corey Nislow

Abstract

Chemical genomics is an interdisciplinary field that combines small molecule perturbation with traditional genomics to understand gene function and to study the mode(s) of drug action. A benefit of chemical genomic screens is their breadth; each screen can capture the sensitivity of comprehensive collections of mutants or, in the case of mammalian cells, gene knock-downs, simultaneously. As with other large-scale experimental platforms, to compare and contrast such profiles, e.g. for clustering known compounds with uncharacterized compounds, a robust means to compare a large cohort of profiles is required. Existing methods for correlating different chemical profiles include diverse statistical discriminant analysis-based methods and specific gene filtering or normalization methods. Though powerful, none are ideal because they typically require one to define the disrupting effects, commonly known as batch effects, to detect true signal from experimental variation. These effects are not always known, and they can mask true biological differences. We present a method, Bucket Evaluations (BE) that surmounts many of these problems and is extensible to other datasets such as those obtained via gene expression profiling and which is platform independent.

X Demographics

X Demographics

The data shown below were collected from the profiles of 2 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 17 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 6%
Brazil 1 6%
Unknown 15 88%

Demographic breakdown

Readers by professional status Count As %
Researcher 6 35%
Student > Ph. D. Student 4 24%
Student > Master 2 12%
Librarian 1 6%
Professor > Associate Professor 1 6%
Other 1 6%
Unknown 2 12%
Readers by discipline Count As %
Agricultural and Biological Sciences 4 24%
Biochemistry, Genetics and Molecular Biology 3 18%
Computer Science 3 18%
Chemistry 2 12%
Mathematics 1 6%
Other 2 12%
Unknown 2 12%
Attention Score in Context

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 29 September 2012.
All research outputs
#6,381,700
of 22,679,690 outputs
Outputs from BMC Bioinformatics
#2,468
of 7,251 outputs
Outputs of similar age
#46,521
of 171,685 outputs
Outputs of similar age from BMC Bioinformatics
#32
of 105 outputs
Altmetric has tracked 22,679,690 research outputs across all sources so far. This one has received more attention than most of these and is in the 70th percentile.
So far Altmetric has tracked 7,251 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one has gotten more attention than average, scoring higher than 64% 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 171,685 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 71% of its contemporaries.
We're also able to compare this research output to 105 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 65% of its contemporaries.