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Investigating the relevance of major signaling pathways in cancer survival using a biologically meaningful deep learning model

Overview of attention for article published in BMC Bioinformatics, February 2021
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About this Attention Score

  • Average Attention Score compared to outputs of the same age

Mentioned by

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5 X users

Citations

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13 Dimensions

Readers on

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40 Mendeley
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Title
Investigating the relevance of major signaling pathways in cancer survival using a biologically meaningful deep learning model
Published in
BMC Bioinformatics, February 2021
DOI 10.1186/s12859-020-03850-6
Pubmed ID
Authors

Jiarui Feng, Heming Zhang, Fuhai Li

X Demographics

X Demographics

The data shown below were collected from the profiles of 5 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 40 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 40 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 8 20%
Student > Master 4 10%
Lecturer 3 8%
Student > Doctoral Student 3 8%
Student > Ph. D. Student 3 8%
Other 5 13%
Unknown 14 35%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 6 15%
Medicine and Dentistry 5 13%
Computer Science 4 10%
Agricultural and Biological Sciences 2 5%
Engineering 2 5%
Other 5 13%
Unknown 16 40%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 06 February 2021.
All research outputs
#14,536,886
of 23,277,141 outputs
Outputs from BMC Bioinformatics
#4,811
of 7,369 outputs
Outputs of similar age
#271,066
of 505,755 outputs
Outputs of similar age from BMC Bioinformatics
#117
of 144 outputs
Altmetric has tracked 23,277,141 research outputs across all sources so far. This one is in the 35th percentile – i.e., 35% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,369 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 30th percentile – i.e., 30% 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 505,755 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 43rd percentile – i.e., 43% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 144 others from the same source and published within six weeks on either side of this one. This one is in the 14th percentile – i.e., 14% of its contemporaries scored the same or lower than it.