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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Feature weight estimation for gene selection: a local hyperlinear learning approach
|
|---|---|
| Published in |
BMC Bioinformatics, March 2014
|
| DOI | 10.1186/1471-2105-15-70 |
| Pubmed ID | |
| Authors |
Hongmin Cai, Peiying Ruan, Michael Ng, Tatsuya Akutsu |
| Abstract |
Modeling high-dimensional data involving thousands of variables is particularly important for gene expression profiling experiments, nevertheless,it remains a challenging task. One of the challenges is to implement an effective method for selecting a small set of relevant genes, buried in high-dimensional irrelevant noises. RELIEF is a popular and widely used approach for feature selection owing to its low computational cost and high accuracy. However, RELIEF based methods suffer from instability, especially in the presence of noisy and/or high-dimensional outliers. |
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 43 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Japan | 1 | 2% |
| Netherlands | 1 | 2% |
| India | 1 | 2% |
| Denmark | 1 | 2% |
| Unknown | 39 | 91% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 7 | 16% |
| Researcher | 7 | 16% |
| Student > Master | 5 | 12% |
| Student > Bachelor | 4 | 9% |
| Student > Postgraduate | 2 | 5% |
| Other | 6 | 14% |
| Unknown | 12 | 28% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 10 | 23% |
| Computer Science | 9 | 21% |
| Engineering | 4 | 9% |
| Medicine and Dentistry | 3 | 7% |
| Biochemistry, Genetics and Molecular Biology | 2 | 5% |
| Other | 3 | 7% |
| Unknown | 12 | 28% |
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 14 March 2014.
All research outputs
#20,223,099
of 22,747,498 outputs
Outputs from BMC Bioinformatics
#6,840
of 7,268 outputs
Outputs of similar age
#189,619
of 220,990 outputs
Outputs of similar age from BMC Bioinformatics
#89
of 99 outputs
Altmetric has tracked 22,747,498 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 220,990 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 99 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.