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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Automated identification of protein-ligand interaction features using Inductive Logic Programming: a hexose binding case study
|
|---|---|
| Published in |
BMC Bioinformatics, July 2012
|
| DOI | 10.1186/1471-2105-13-162 |
| Pubmed ID | |
| Authors |
Jose C A Santos, Houssam Nassif, David Page, Stephen H Muggleton, Michael J E Sternberg |
| Abstract |
There is a need for automated methods to learn general features of the interactions of a ligand class with its diverse set of protein receptors. An appropriate machine learning approach is Inductive Logic Programming (ILP), which automatically generates comprehensible rules in addition to prediction. The development of ILP systems which can learn rules of the complexity required for studies on protein structure remains a challenge. In this work we use a new ILP system, ProGolem, and demonstrate its performance on learning features of hexose-protein interactions. |
X Demographics
The data shown below were collected from the profiles of 4 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Spain | 1 | 25% |
| Unknown | 3 | 75% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 3 | 75% |
| Scientists | 1 | 25% |
Mendeley readers
The data shown below were compiled from readership statistics for 55 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| France | 1 | 2% |
| Brazil | 1 | 2% |
| United Kingdom | 1 | 2% |
| Mexico | 1 | 2% |
| China | 1 | 2% |
| Spain | 1 | 2% |
| United States | 1 | 2% |
| Unknown | 48 | 87% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 15 | 27% |
| Student > Ph. D. Student | 10 | 18% |
| Professor > Associate Professor | 9 | 16% |
| Student > Master | 4 | 7% |
| Student > Postgraduate | 3 | 5% |
| Other | 8 | 15% |
| Unknown | 6 | 11% |
| Readers by discipline | Count | As % |
|---|---|---|
| Chemistry | 11 | 20% |
| Agricultural and Biological Sciences | 9 | 16% |
| Computer Science | 9 | 16% |
| Biochemistry, Genetics and Molecular Biology | 6 | 11% |
| Medicine and Dentistry | 6 | 11% |
| Other | 5 | 9% |
| Unknown | 9 | 16% |
Attention Score in Context
This research output has an Altmetric Attention Score of 8. 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 10 July 2020.
All research outputs
#4,080,991
of 22,687,320 outputs
Outputs from BMC Bioinformatics
#1,583
of 7,252 outputs
Outputs of similar age
#28,237
of 164,341 outputs
Outputs of similar age from BMC Bioinformatics
#18
of 94 outputs
Altmetric has tracked 22,687,320 research outputs across all sources so far. Compared to these this one has done well and is in the 81st percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,252 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 78% 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 164,341 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 82% of its contemporaries.
We're also able to compare this research output to 94 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 81% of its contemporaries.