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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Comprehensive Decision Tree Models in Bioinformatics
|
|---|---|
| Published in |
PLOS ONE, March 2012
|
| DOI | 10.1371/journal.pone.0033812 |
| Pubmed ID | |
| Authors |
Gregor Stiglic, Simon Kocbek, Igor Pernek, Peter Kokol |
| Abstract |
Classification is an important and widely used machine learning technique in bioinformatics. Researchers and other end-users of machine learning software often prefer to work with comprehensible models where knowledge extraction and explanation of reasoning behind the classification model are possible. |
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% |
| United States | 1 | 33% |
| Egypt | 1 | 33% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 2 | 67% |
| Members of the public | 1 | 33% |
Mendeley readers
The data shown below were compiled from readership statistics for 145 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 5 | 3% |
| Korea, Republic of | 1 | <1% |
| United Kingdom | 1 | <1% |
| Brazil | 1 | <1% |
| Slovenia | 1 | <1% |
| Canada | 1 | <1% |
| Unknown | 135 | 93% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 29 | 20% |
| Student > Master | 24 | 17% |
| Researcher | 22 | 15% |
| Student > Doctoral Student | 12 | 8% |
| Student > Bachelor | 12 | 8% |
| Other | 23 | 16% |
| Unknown | 23 | 16% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 37 | 26% |
| Agricultural and Biological Sciences | 20 | 14% |
| Engineering | 14 | 10% |
| Medicine and Dentistry | 12 | 8% |
| Social Sciences | 6 | 4% |
| Other | 28 | 19% |
| Unknown | 28 | 19% |
Attention Score in Context
This research output has an Altmetric Attention Score of 2. 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 06 February 2014.
All research outputs
#14,143,704
of 22,664,267 outputs
Outputs from PLOS ONE
#115,535
of 193,506 outputs
Outputs of similar age
#94,506
of 160,569 outputs
Outputs of similar age from PLOS ONE
#2,068
of 3,721 outputs
Altmetric has tracked 22,664,267 research outputs across all sources so far. This one is in the 35th percentile – i.e., 35% of other outputs scored the same or lower than it.
So far Altmetric has tracked 193,506 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 15.0. This one is in the 36th percentile – i.e., 36% 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 160,569 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 38th percentile – i.e., 38% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 3,721 others from the same source and published within six weeks on either side of this one. This one is in the 40th percentile – i.e., 40% of its contemporaries scored the same or lower than it.