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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Combining techniques for screening and evaluating interaction terms on high-dimensional time-to-event data
|
|---|---|
| Published in |
BMC Bioinformatics, February 2014
|
| DOI | 10.1186/1471-2105-15-58 |
| Pubmed ID | |
| Authors |
Murat Sariyar, Isabell Hoffmann, Harald Binder |
| Abstract |
Molecular data, e.g. arising from microarray technology, is often used for predicting survival probabilities of patients. For multivariate risk prediction models on such high-dimensional data, there are established techniques that combine parameter estimation and variable selection. One big challenge is to incorporate interactions into such prediction models. In this feasibility study, we present building blocks for evaluating and incorporating interactions terms in high-dimensional time-to-event settings, especially for settings in which it is computationally too expensive to check all possible interactions. |
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.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Norway | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 28 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Netherlands | 1 | 4% |
| Germany | 1 | 4% |
| Belgium | 1 | 4% |
| Canada | 1 | 4% |
| Unknown | 24 | 86% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 12 | 43% |
| Researcher | 11 | 39% |
| Professor | 2 | 7% |
| Lecturer | 2 | 7% |
| Student > Master | 1 | 4% |
| Other | 0 | 0% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 8 | 29% |
| Agricultural and Biological Sciences | 7 | 25% |
| Mathematics | 5 | 18% |
| Biochemistry, Genetics and Molecular Biology | 3 | 11% |
| Environmental Science | 1 | 4% |
| Other | 3 | 11% |
| Unknown | 1 | 4% |
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 27 February 2014.
All research outputs
#20,221,866
of 22,745,803 outputs
Outputs from BMC Bioinformatics
#6,840
of 7,268 outputs
Outputs of similar age
#189,810
of 221,189 outputs
Outputs of similar age from BMC Bioinformatics
#97
of 107 outputs
Altmetric has tracked 22,745,803 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,268 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 1st percentile – i.e., 1% 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 221,189 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 107 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.