Title |
Self-organizing ontology of biochemically relevant small molecules
|
---|---|
Published in |
BMC Bioinformatics, January 2012
|
DOI | 10.1186/1471-2105-13-3 |
Pubmed ID | |
Authors |
Leonid L Chepelev, Janna Hastings, Marcus Ennis, Christoph Steinbeck, Michel Dumontier |
Abstract |
The advent of high-throughput experimentation in biochemistry has led to the generation of vast amounts of chemical data, necessitating the development of novel analysis, characterization, and cataloguing techniques and tools. Recently, a movement to publically release such data has advanced biochemical structure-activity relationship research, while providing new challenges, the biggest being the curation, annotation, and classification of this information to facilitate useful biochemical pattern analysis. Unfortunately, the human resources currently employed by the organizations supporting these efforts (e.g. ChEBI) are expanding linearly, while new useful scientific information is being released in a seemingly exponential fashion. Compounding this, currently existing chemical classification and annotation systems are not amenable to automated classification, formal and transparent chemical class definition axiomatization, facile class redefinition, or novel class integration, thus further limiting chemical ontology growth by necessitating human involvement in curation. Clearly, there is a need for the automation of this process, especially for novel chemical entities of biological interest. |
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Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 1 | 100% |
Demographic breakdown
Type | Count | As % |
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Scientists | 1 | 100% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 7 | 10% |
Brazil | 1 | 1% |
India | 1 | 1% |
China | 1 | 1% |
Mexico | 1 | 1% |
Japan | 1 | 1% |
Russia | 1 | 1% |
Unknown | 60 | 82% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Researcher | 29 | 40% |
Student > Ph. D. Student | 15 | 21% |
Other | 8 | 11% |
Student > Master | 6 | 8% |
Professor > Associate Professor | 4 | 5% |
Other | 9 | 12% |
Unknown | 2 | 3% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 25 | 34% |
Computer Science | 18 | 25% |
Chemistry | 6 | 8% |
Biochemistry, Genetics and Molecular Biology | 5 | 7% |
Engineering | 4 | 5% |
Other | 10 | 14% |
Unknown | 5 | 7% |