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X Demographics
Mendeley readers
Attention Score in Context
| Title |
A comprehensive comparison of random forests and support vector machines for microarray-based cancer classification
|
|---|---|
| Published in |
BMC Bioinformatics, July 2008
|
| DOI | 10.1186/1471-2105-9-319 |
| Pubmed ID | |
| Authors |
Alexander Statnikov, Lily Wang, Constantin F Aliferis |
| Abstract |
Cancer diagnosis and clinical outcome prediction are among the most important emerging applications of gene expression microarray technology with several molecular signatures on their way toward clinical deployment. Use of the most accurate classification algorithms available for microarray gene expression data is a critical ingredient in order to develop the best possible molecular signatures for patient care. As suggested by a large body of literature to date, support vector machines can be considered "best of class" algorithms for classification of such data. Recent work, however, suggests that random forest classifiers may outperform support vector machines in this domain. |
X Demographics
The data shown below were collected from the profiles of 3 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Germany | 1 | 33% |
| Belgium | 1 | 33% |
| Unknown | 1 | 33% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 2 | 67% |
| Scientists | 1 | 33% |
Mendeley readers
The data shown below were compiled from readership statistics for 561 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 11 | 2% |
| Canada | 6 | 1% |
| France | 5 | <1% |
| Sweden | 4 | <1% |
| Germany | 4 | <1% |
| Spain | 3 | <1% |
| Netherlands | 2 | <1% |
| China | 2 | <1% |
| Malaysia | 2 | <1% |
| Other | 8 | 1% |
| Unknown | 514 | 92% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 140 | 25% |
| Researcher | 82 | 15% |
| Student > Master | 74 | 13% |
| Student > Bachelor | 41 | 7% |
| Student > Doctoral Student | 25 | 4% |
| Other | 92 | 16% |
| Unknown | 107 | 19% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 115 | 20% |
| Agricultural and Biological Sciences | 90 | 16% |
| Engineering | 53 | 9% |
| Biochemistry, Genetics and Molecular Biology | 45 | 8% |
| Medicine and Dentistry | 26 | 5% |
| Other | 100 | 18% |
| Unknown | 132 | 24% |
Attention Score in Context
This research output has an Altmetric Attention Score of 10. 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 July 2013.
All research outputs
#3,073,561
of 22,663,969 outputs
Outputs from BMC Bioinformatics
#1,106
of 7,247 outputs
Outputs of similar age
#9,433
of 81,781 outputs
Outputs of similar age from BMC Bioinformatics
#4
of 29 outputs
Altmetric has tracked 22,663,969 research outputs across all sources so far. Compared to these this one has done well and is in the 86th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,247 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one has done well, scoring higher than 84% of its peers.
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 81,781 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 88% of its contemporaries.
We're also able to compare this research output to 29 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 86% of its contemporaries.