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Accounting for grouped predictor variables or pathways in high-dimensional penalized Cox regression models

Overview of attention for article published in BMC Bioinformatics, July 2020
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  • Average Attention Score compared to outputs of the same age

Mentioned by

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2 X users

Citations

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15 Dimensions

Readers on

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17 Mendeley
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Title
Accounting for grouped predictor variables or pathways in high-dimensional penalized Cox regression models
Published in
BMC Bioinformatics, July 2020
DOI 10.1186/s12859-020-03618-y
Pubmed ID
Authors

Shaima Belhechmi, Riccardo De Bin, Federico Rotolo, Stefan Michiels

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 %
Unknown 17 100%

Demographic breakdown

Readers by professional status Count As %
Student > Doctoral Student 3 18%
Student > Master 3 18%
Researcher 2 12%
Student > Ph. D. Student 2 12%
Professor 1 6%
Other 2 12%
Unknown 4 24%
Readers by discipline Count As %
Computer Science 3 18%
Mathematics 2 12%
Nursing and Health Professions 1 6%
Biochemistry, Genetics and Molecular Biology 1 6%
Economics, Econometrics and Finance 1 6%
Other 3 18%
Unknown 6 35%
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 10 July 2020.
All research outputs
#15,086,839
of 23,220,133 outputs
Outputs from BMC Bioinformatics
#5,112
of 7,358 outputs
Outputs of similar age
#236,334
of 398,655 outputs
Outputs of similar age from BMC Bioinformatics
#99
of 127 outputs
Altmetric has tracked 23,220,133 research outputs across all sources so far. This one is in the 32nd percentile – i.e., 32% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,358 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 25th percentile – i.e., 25% 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 398,655 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 37th percentile – i.e., 37% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 127 others from the same source and published within six weeks on either side of this one. This one is in the 18th percentile – i.e., 18% of its contemporaries scored the same or lower than it.