Title |
Which gene did you mean?
|
---|---|
Published in |
BMC Bioinformatics, June 2005
|
DOI | 10.1186/1471-2105-6-142 |
Pubmed ID | |
Authors |
Barend Mons |
Abstract |
Computational Biology needs computer-readable information records. Increasingly, meta-analysed and pre-digested information is being used in the follow up of high throughput experiments and other investigations that yield massive data sets. Semantic enrichment of plain text is crucial for computer aided analysis. In general people will think about semantic tagging as just another form of text mining, and that term has quite a negative connotation in the minds of some biologists who have been disappointed by classical approaches of text mining. Efforts so far have tried to develop tools and technologies that retrospectively extract the correct information from text, which is usually full of ambiguities. Although remarkable results have been obtained in experimental circumstances, the wide spread use of information mining tools is lagging behind earlier expectations. This commentary proposes to make semantic tagging an integral process to electronic publishing. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
Germany | 3 | 18% |
United States | 3 | 18% |
Sweden | 2 | 12% |
Netherlands | 1 | 6% |
United Kingdom | 1 | 6% |
Italy | 1 | 6% |
Hong Kong | 1 | 6% |
Uganda | 1 | 6% |
Unknown | 4 | 24% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 11 | 65% |
Scientists | 5 | 29% |
Science communicators (journalists, bloggers, editors) | 1 | 6% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
United Kingdom | 3 | 5% |
Spain | 2 | 3% |
Malaysia | 1 | 2% |
Indonesia | 1 | 2% |
Sweden | 1 | 2% |
Switzerland | 1 | 2% |
Uruguay | 1 | 2% |
Philippines | 1 | 2% |
Unknown | 51 | 82% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Researcher | 16 | 26% |
Student > Ph. D. Student | 7 | 11% |
Student > Master | 6 | 10% |
Student > Doctoral Student | 6 | 10% |
Professor | 4 | 6% |
Other | 16 | 26% |
Unknown | 7 | 11% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 21 | 34% |
Computer Science | 12 | 19% |
Arts and Humanities | 6 | 10% |
Medicine and Dentistry | 5 | 8% |
Biochemistry, Genetics and Molecular Biology | 3 | 5% |
Other | 8 | 13% |
Unknown | 7 | 11% |