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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Feature selection and classifier performance on diverse bio- logical datasets
|
|---|---|
| Published in |
BMC Bioinformatics, November 2014
|
| DOI | 10.1186/1471-2105-15-s13-s4 |
| Pubmed ID | |
| Authors |
Edward Hemphill, James Lindsay, Chih Lee, Ion I Măndoiu, Craig E Nelson |
| Abstract |
There is an ever-expanding range of technologies that generate very large numbers of biomarkers for research and clinical applications. Choosing the most informative biomarkers from a high-dimensional data set, combined with identifying the most reliable and accurate classification algorithms to use with that biomarker set, can be a daunting task. Existing surveys of feature selection and classification algorithms typically focus on a single data type, such as gene expression microarrays, and rarely explore the model's performance across multiple biological data types. |
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 67 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| India | 1 | 1% |
| United States | 1 | 1% |
| Belgium | 1 | 1% |
| Unknown | 64 | 96% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 17 | 25% |
| Student > Master | 15 | 22% |
| Researcher | 14 | 21% |
| Student > Bachelor | 3 | 4% |
| Student > Postgraduate | 3 | 4% |
| Other | 6 | 9% |
| Unknown | 9 | 13% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 18 | 27% |
| Biochemistry, Genetics and Molecular Biology | 17 | 25% |
| Agricultural and Biological Sciences | 7 | 10% |
| Engineering | 5 | 7% |
| Medicine and Dentistry | 4 | 6% |
| Other | 4 | 6% |
| Unknown | 12 | 18% |
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 02 December 2014.
All research outputs
#20,245,139
of 22,772,779 outputs
Outputs from BMC Bioinformatics
#6,848
of 7,276 outputs
Outputs of similar age
#215,953
of 258,737 outputs
Outputs of similar age from BMC Bioinformatics
#128
of 135 outputs
Altmetric has tracked 22,772,779 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 7,276 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 258,737 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 135 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.