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Automated identification of cell-type-specific genes in the mouse brain by image computing of expression patterns

Overview of attention for article published in BMC Bioinformatics, June 2014
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Title
Automated identification of cell-type-specific genes in the mouse brain by image computing of expression patterns
Published in
BMC Bioinformatics, June 2014
DOI 10.1186/1471-2105-15-209
Pubmed ID
Authors

Rongjian Li, Wenlu Zhang, Shuiwang Ji

Abstract

Differential gene expression patterns in cells of the mammalian brain result in the morphological, connectional, and functional diversity of cells. A wide variety of studies have shown that certain genes are expressed only in specific cell-types. Analysis of cell-type-specific gene expression patterns can provide insights into the relationship between genes, connectivity, brain regions, and cell-types. However, automated methods for identifying cell-type-specific genes are lacking to date.

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X Demographics

The data shown below were collected from the profiles of 3 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 23 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Italy 1 4%
Norway 1 4%
Unknown 21 91%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 10 43%
Researcher 6 26%
Other 2 9%
Student > Master 2 9%
Student > Bachelor 1 4%
Other 0 0%
Unknown 2 9%
Readers by discipline Count As %
Agricultural and Biological Sciences 9 39%
Computer Science 5 22%
Biochemistry, Genetics and Molecular Biology 3 13%
Neuroscience 3 13%
Physics and Astronomy 1 4%
Other 0 0%
Unknown 2 9%
Attention Score in Context

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 20 June 2014.
All research outputs
#17,722,431
of 22,757,541 outputs
Outputs from BMC Bioinformatics
#5,926
of 7,272 outputs
Outputs of similar age
#155,850
of 228,326 outputs
Outputs of similar age from BMC Bioinformatics
#105
of 154 outputs
Altmetric has tracked 22,757,541 research outputs across all sources so far. This one is in the 19th percentile – i.e., 19% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,272 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. 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 228,326 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 28th percentile – i.e., 28% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 154 others from the same source and published within six weeks on either side of this one. This one is in the 23rd percentile – i.e., 23% of its contemporaries scored the same or lower than it.