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PBSeq: Modeling base-level bias to estimate gene and isoform expression for RNA-seq data

Overview of attention for article published in International Journal of Machine Learning and Cybernetics, March 2016
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1 X user

Citations

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1 Dimensions

Readers on

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3 Mendeley
Title
PBSeq: Modeling base-level bias to estimate gene and isoform expression for RNA-seq data
Published in
International Journal of Machine Learning and Cybernetics, March 2016
DOI 10.1007/s13042-016-0497-z
Authors

Li Zhang, Xuejun Liu

X Demographics

X Demographics

The data shown below were collected from the profile of 1 X user 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 3 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 33%
Unknown 2 67%

Demographic breakdown

Readers by professional status Count As %
Researcher 2 67%
Professor > Associate Professor 1 33%
Readers by discipline Count As %
Agricultural and Biological Sciences 2 67%
Computer Science 1 33%
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 08 March 2016.
All research outputs
#18,445,779
of 22,854,458 outputs
Outputs from International Journal of Machine Learning and Cybernetics
#229
of 435 outputs
Outputs of similar age
#216,891
of 298,622 outputs
Outputs of similar age from International Journal of Machine Learning and Cybernetics
#2
of 2 outputs
Altmetric has tracked 22,854,458 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 435 research outputs from this source. They receive a mean Attention Score of 1.5. This one is in the 41st percentile – i.e., 41% 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 298,622 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 15th percentile – i.e., 15% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 2 others from the same source and published within six weeks on either side of this one.