Title |
BioAssay Ontology (BAO): a semantic description of bioassays and high-throughput screening results
|
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Published in |
BMC Bioinformatics, June 2011
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DOI | 10.1186/1471-2105-12-257 |
Pubmed ID | |
Authors |
Ubbo Visser, Saminda Abeyruwan, Uma Vempati, Robin P Smith, Vance Lemmon, Stephan C Schürer |
Abstract |
High-throughput screening (HTS) is one of the main strategies to identify novel entry points for the development of small molecule chemical probes and drugs and is now commonly accessible to public sector research. Large amounts of data generated in HTS campaigns are submitted to public repositories such as PubChem, which is growing at an exponential rate. The diversity and quantity of available HTS assays and screening results pose enormous challenges to organizing, standardizing, integrating, and analyzing the datasets and thus to maximize the scientific and ultimately the public health impact of the huge investments made to implement public sector HTS capabilities. Novel approaches to organize, standardize and access HTS data are required to address these challenges. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 3 | 100% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 2 | 67% |
Scientists | 1 | 33% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 4 | 4% |
United Kingdom | 3 | 3% |
Netherlands | 2 | 2% |
Brazil | 2 | 2% |
France | 1 | <1% |
Bulgaria | 1 | <1% |
Switzerland | 1 | <1% |
Germany | 1 | <1% |
Japan | 1 | <1% |
Other | 1 | <1% |
Unknown | 93 | 85% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Researcher | 35 | 32% |
Student > Ph. D. Student | 21 | 19% |
Other | 9 | 8% |
Student > Master | 9 | 8% |
Student > Bachelor | 7 | 6% |
Other | 21 | 19% |
Unknown | 8 | 7% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 29 | 26% |
Computer Science | 24 | 22% |
Chemistry | 16 | 15% |
Engineering | 8 | 7% |
Medicine and Dentistry | 5 | 5% |
Other | 15 | 14% |
Unknown | 13 | 12% |