Title |
Structure-based classification and ontology in chemistry
|
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Published in |
Journal of Cheminformatics, April 2012
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DOI | 10.1186/1758-2946-4-8 |
Pubmed ID | |
Authors |
Janna Hastings, Despoina Magka, Colin Batchelor, Lian Duan, Robert Stevens, Marcus Ennis, Christoph Steinbeck |
Abstract |
Recent years have seen an explosion in the availability of data in the chemistry domain. With this information explosion, however, retrieving relevant results from the available information, and organising those results, become even harder problems. Computational processing is essential to filter and organise the available resources so as to better facilitate the work of scientists. Ontologies encode expert domain knowledge in a hierarchically organised machine-processable format. One such ontology for the chemical domain is ChEBI. ChEBI provides a classification of chemicals based on their structural features and a role or activity-based classification. An example of a structure-based class is 'pentacyclic compound' (compounds containing five-ring structures), while an example of a role-based class is 'analgesic', since many different chemicals can act as analgesics without sharing structural features. Structure-based classification in chemistry exploits elegant regularities and symmetries in the underlying chemical domain. As yet, there has been neither a systematic analysis of the types of structural classification in use in chemistry nor a comparison to the capabilities of available technologies. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 2 | 20% |
United Kingdom | 2 | 20% |
Spain | 1 | 10% |
Germany | 1 | 10% |
Unknown | 4 | 40% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 7 | 70% |
Scientists | 3 | 30% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 2 | 2% |
Portugal | 1 | 1% |
Germany | 1 | 1% |
France | 1 | 1% |
Italy | 1 | 1% |
Brazil | 1 | 1% |
Netherlands | 1 | 1% |
United Kingdom | 1 | 1% |
India | 1 | 1% |
Other | 2 | 2% |
Unknown | 86 | 88% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Researcher | 30 | 31% |
Student > Ph. D. Student | 22 | 22% |
Student > Master | 11 | 11% |
Student > Postgraduate | 5 | 5% |
Other | 5 | 5% |
Other | 11 | 11% |
Unknown | 14 | 14% |
Readers by discipline | Count | As % |
---|---|---|
Computer Science | 23 | 23% |
Agricultural and Biological Sciences | 19 | 19% |
Chemistry | 17 | 17% |
Biochemistry, Genetics and Molecular Biology | 6 | 6% |
Pharmacology, Toxicology and Pharmaceutical Science | 3 | 3% |
Other | 11 | 11% |
Unknown | 19 | 19% |