| Title |
Feature selection and survival modeling in The Cancer Genome Atlas
|
|---|---|
| Published in |
International Journal of Nanomedicine, September 2013
|
| DOI | 10.2147/ijn.s40733 |
| Pubmed ID | |
| Authors |
Hyunsoo Kim, Markus Bredel |
| Abstract |
Personalized medicine is predicated on the concept of identifying subgroups of a common disease for better treatment. Identifying biomarkers that predict disease subtypes has been a major focus of biomedical science. In the era of genome-wide profiling, there is controversy as to the optimal number of genes as an input of a feature selection algorithm for survival modeling. |
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.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 1 | 50% |
| Unknown | 1 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 1 | 50% |
| Practitioners (doctors, other healthcare professionals) | 1 | 50% |
Mendeley readers
The data shown below were compiled from readership statistics for 50 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Netherlands | 1 | 2% |
| Denmark | 1 | 2% |
| Germany | 1 | 2% |
| Unknown | 47 | 94% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 13 | 26% |
| Student > Ph. D. Student | 11 | 22% |
| Student > Postgraduate | 4 | 8% |
| Student > Bachelor | 3 | 6% |
| Other | 3 | 6% |
| Other | 8 | 16% |
| Unknown | 8 | 16% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 13 | 26% |
| Agricultural and Biological Sciences | 10 | 20% |
| Biochemistry, Genetics and Molecular Biology | 8 | 16% |
| Computer Science | 4 | 8% |
| Engineering | 2 | 4% |
| Other | 5 | 10% |
| Unknown | 8 | 16% |
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 19 September 2013.
All research outputs
#19,942,887
of 25,371,288 outputs
Outputs from International Journal of Nanomedicine
#2,970
of 4,123 outputs
Outputs of similar age
#154,571
of 212,462 outputs
Outputs of similar age from International Journal of Nanomedicine
#91
of 104 outputs
Altmetric has tracked 25,371,288 research outputs across all sources so far. This one is in the 18th percentile – i.e., 18% of other outputs scored the same or lower than it.
So far Altmetric has tracked 4,123 research outputs from this source. They receive a mean Attention Score of 4.7. This one is in the 24th percentile – i.e., 24% 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 212,462 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 24th percentile – i.e., 24% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 104 others from the same source and published within six weeks on either side of this one. This one is in the 10th percentile – i.e., 10% of its contemporaries scored the same or lower than it.