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Automated identification of protein-ligand interaction features using Inductive Logic Programming: a hexose binding case study

Overview of attention for article published in BMC Bioinformatics, January 2012
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (83rd percentile)
  • Good Attention Score compared to outputs of the same age and source (76th percentile)

Mentioned by

twitter
5 tweeters
wikipedia
1 Wikipedia page
q&a
1 Q&A thread

Citations

dimensions_citation
11 Dimensions

Readers on

mendeley
41 Mendeley
citeulike
1 CiteULike
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Title
Automated identification of protein-ligand interaction features using Inductive Logic Programming: a hexose binding case study
Published in
BMC Bioinformatics, January 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.

Twitter Demographics

The data shown below were collected from the profiles of 5 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 41 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 34 83%

Demographic breakdown

Readers by professional status Count As %
Researcher 13 32%
Student > Ph. D. Student 9 22%
Professor > Associate Professor 6 15%
Student > Master 4 10%
Student > Postgraduate 3 7%
Other 5 12%
Unknown 1 2%
Readers by discipline Count As %
Chemistry 10 24%
Agricultural and Biological Sciences 9 22%
Computer Science 8 20%
Medicine and Dentistry 5 12%
Biochemistry, Genetics and Molecular Biology 3 7%
Other 2 5%
Unknown 4 10%

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 16 December 2018.
All research outputs
#2,232,472
of 14,039,404 outputs
Outputs from BMC Bioinformatics
#964
of 5,236 outputs
Outputs of similar age
#23,889
of 146,384 outputs
Outputs of similar age from BMC Bioinformatics
#6
of 25 outputs
Altmetric has tracked 14,039,404 research outputs across all sources so far. Compared to these this one has done well and is in the 84th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 5,236 research outputs from this source. They receive a mean Attention Score of 4.9. This one has done well, scoring higher than 81% 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 146,384 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 83% of its contemporaries.
We're also able to compare this research output to 25 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 76% of its contemporaries.