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AMBIT RESTful web services: an implementation of the OpenTox application programming interface

Overview of attention for article published in Journal of Cheminformatics, May 2011
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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 (91st percentile)
  • Good Attention Score compared to outputs of the same age and source (78th percentile)

Mentioned by

blogs
1 blog
twitter
5 tweeters
googleplus
2 Google+ users

Citations

dimensions_citation
41 Dimensions

Readers on

mendeley
83 Mendeley
citeulike
4 CiteULike
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Title
AMBIT RESTful web services: an implementation of the OpenTox application programming interface
Published in
Journal of Cheminformatics, May 2011
DOI 10.1186/1758-2946-3-18
Pubmed ID
Authors

Nina Jeliazkova, Vedrin Jeliazkov

Abstract

The AMBIT web services package is one of the several existing independent implementations of the OpenTox Application Programming Interface and is built according to the principles of the Representational State Transfer (REST) architecture. The Open Source Predictive Toxicology Framework, developed by the partners in the EC FP7 OpenTox project, aims at providing a unified access to toxicity data and predictive models, as well as validation procedures. This is achieved by i) an information model, based on a common OWL-DL ontology ii) links to related ontologies; iii) data and algorithms, available through a standardized REST web services interface, where every compound, data set or predictive method has a unique web address, used to retrieve its Resource Description Framework (RDF) representation, or initiate the associated calculations.The AMBIT web services package has been developed as an extension of AMBIT modules, adding the ability to create (Quantitative) Structure-Activity Relationship (QSAR) models and providing an OpenTox API compliant interface. The representation of data and processing resources in W3C Resource Description Framework facilitates integrating the resources as Linked Data. By uploading datasets with chemical structures and arbitrary set of properties, they become automatically available online in several formats. The services provide unified interfaces to several descriptor calculation, machine learning and similarity searching algorithms, as well as to applicability domain and toxicity prediction models. All Toxtree modules for predicting the toxicological hazard of chemical compounds are also integrated within this package. The complexity and diversity of the processing is reduced to the simple paradigm "read data from a web address, perform processing, write to a web address". The online service allows to easily run predictions, without installing any software, as well to share online datasets and models. The downloadable web application allows researchers to setup an arbitrary number of service instances for specific purposes and at suitable locations. These services could be used as a distributed framework for processing of resource-intensive tasks and data sharing or in a fully independent way, according to the specific needs. The advantage of exposing the functionality via the OpenTox API is seamless interoperability, not only within a single web application, but also in a network of distributed services. Last, but not least, the services provide a basis for building web mashups, end user applications with friendly GUIs, as well as embedding the functionalities in existing workflow systems.

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 83 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Bulgaria 3 4%
Canada 2 2%
United Kingdom 2 2%
Portugal 1 1%
Netherlands 1 1%
Cyprus 1 1%
Brazil 1 1%
Sweden 1 1%
Germany 1 1%
Other 3 4%
Unknown 67 81%

Demographic breakdown

Readers by professional status Count As %
Researcher 28 34%
Student > Ph. D. Student 15 18%
Student > Bachelor 9 11%
Other 7 8%
Student > Master 6 7%
Other 15 18%
Unknown 3 4%
Readers by discipline Count As %
Computer Science 24 29%
Chemistry 24 29%
Agricultural and Biological Sciences 8 10%
Engineering 5 6%
Biochemistry, Genetics and Molecular Biology 3 4%
Other 13 16%
Unknown 6 7%

Attention Score in Context

This research output has an Altmetric Attention Score of 14. 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 May 2018.
All research outputs
#1,043,377
of 12,936,827 outputs
Outputs from Journal of Cheminformatics
#109
of 517 outputs
Outputs of similar age
#1,011,156
of 12,352,067 outputs
Outputs of similar age from Journal of Cheminformatics
#109
of 517 outputs
Altmetric has tracked 12,936,827 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 91st percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 517 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 9.2. 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 12,352,067 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 91% of its contemporaries.
We're also able to compare this research output to 517 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 78% of its contemporaries.