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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Stepwise classification of cancer samples using clinical and molecular data
|
|---|---|
| Published in |
BMC Bioinformatics, October 2011
|
| DOI | 10.1186/1471-2105-12-422 |
| Pubmed ID | |
| Authors |
Askar Obulkasim, Gerrit A Meijer, Mark A van de Wiel |
| Abstract |
Combining clinical and molecular data types may potentially improve prediction accuracy of a classifier. However, currently there is a shortage of effective and efficient statistical and bioinformatic tools for true integrative data analysis. Existing integrative classifiers have two main disadvantages: First, coarse combination may lead to subtle contributions of one data type to be overshadowed by more obvious contributions of the other. Second, the need to measure both data types for all patients may be both unpractical and (cost) inefficient. |
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 % |
|---|---|---|
| Unknown | 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 39 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 5 | 13% |
| Germany | 1 | 3% |
| Canada | 1 | 3% |
| Unknown | 32 | 82% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 14 | 36% |
| Student > Ph. D. Student | 9 | 23% |
| Other | 4 | 10% |
| Student > Doctoral Student | 2 | 5% |
| Student > Master | 2 | 5% |
| Other | 4 | 10% |
| Unknown | 4 | 10% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 14 | 36% |
| Medicine and Dentistry | 7 | 18% |
| Computer Science | 4 | 10% |
| Mathematics | 3 | 8% |
| Biochemistry, Genetics and Molecular Biology | 2 | 5% |
| Other | 5 | 13% |
| Unknown | 4 | 10% |
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 31 October 2011.
All research outputs
#15,237,301
of 22,655,397 outputs
Outputs from BMC Bioinformatics
#5,353
of 7,236 outputs
Outputs of similar age
#95,964
of 140,785 outputs
Outputs of similar age from BMC Bioinformatics
#70
of 99 outputs
Altmetric has tracked 22,655,397 research outputs across all sources so far. This one is in the 22nd percentile – i.e., 22% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,236 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 18th percentile – i.e., 18% 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 140,785 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 19th percentile – i.e., 19% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 99 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.