Report for: GAGE: generally applicable gene set enrichment for pathway analysis

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Title	GAGE: generally applicable gene set enrichment for pathway analysis
Published in	BMC Bioinformatics, May 2009
DOI	10.1186/1471-2105-10-161
Pubmed ID	19473525
Authors	Weijun Luo, Michael S Friedman, Kerby Shedden, Kurt D Hankenson, Peter J Woolf
Abstract	Gene set analysis (GSA) is a widely used strategy for gene expression data analysis based on pathway knowledge. GSA focuses on sets of related genes and has established major advantages over individual gene analyses, including greater robustness, sensitivity and biological relevance. However, previous GSA methods have limited usage as they cannot handle datasets of different sample sizes or experimental designs.

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Mendeley readers

The data shown below were compiled from readership statistics for 970 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country	Count	As %
United States	17	2%
United Kingdom	14	1%
Germany	6	<1%
Sweden	3	<1%
Netherlands	2	<1%
Portugal	2	<1%
Korea, Republic of	2	<1%
Italy	2	<1%
Russia	2	<1%
Other	13	1%
Unknown	907	94%

Demographic breakdown

Readers by professional status	Count	As %
Student > Ph. D. Student	256	26%
Researcher	226	23%
Student > Master	112	12%
Student > Doctoral Student	62	6%
Student > Bachelor	50	5%
Other	137	14%
Unknown	127	13%

Readers by discipline	Count	As %
Agricultural and Biological Sciences	328	34%
Biochemistry, Genetics and Molecular Biology	206	21%
Medicine and Dentistry	75	8%
Computer Science	43	4%
Immunology and Microbiology	42	4%
Other	117	12%
Unknown	159	16%

Attention Score in Context

This research output has an Altmetric Attention Score of 16. 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 June 2020.

All research outputs

#1,886,937

of 22,787,797 outputs

Outputs from BMC Bioinformatics

#467

of 7,279 outputs

Outputs of similar age

#6,479

of 111,865 outputs

Outputs of similar age from BMC Bioinformatics

of 36 outputs

Altmetric has tracked 22,787,797 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 91st percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.

So far Altmetric has tracked 7,279 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 done particularly well, scoring higher than 93% 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 111,865 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 94% of its contemporaries.

We're also able to compare this research output to 36 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 88% of its contemporaries.

GAGE: generally applicable gene set enrichment for pathway analysis

About this Attention Score

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Geographical breakdown

Demographic breakdown

Attention Score in Context