| Chapter title |
Data Mining Techniques for the Life Sciences
|
|---|---|
| Chapter number | 19 |
| Book title |
Data Mining Techniques for the Life Sciences
|
| Published in |
Methods in molecular biology, January 2016
|
| DOI | 10.1007/978-1-4939-3572-7_19 |
| Pubmed ID | |
| Book ISBNs |
978-1-4939-3570-3, 978-1-4939-3572-7
|
| Authors |
Hoehndorf, Robert, Gkoutos, Georgios V, Schofield, Paul N, Robert Hoehndorf, Georgios V. Gkoutos, Paul N. Schofield, Gkoutos, Georgios V., Schofield, Paul N. |
| Editors |
Oliviero Carugo, Frank Eisenhaber |
| Abstract |
The use of ontologies has increased rapidly over the past decade and they now provide a key component of most major databases in biology and biomedicine. Consequently, datamining over these databases benefits from considering the specific structure and content of ontologies, and several methods have been developed to use ontologies in datamining applications. Here, we discuss the principles of ontology structure, and datamining methods that rely on ontologies. The impact of these methods in the biological and biomedical sciences has been profound and is likely to increase as more datasets are becoming available using common, shared ontologies. |
Mendeley readers
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Indonesia | 1 | 2% |
| Italy | 1 | 2% |
| Brazil | 1 | 2% |
| India | 1 | 2% |
| Czechia | 1 | 2% |
| Japan | 1 | 2% |
| United States | 1 | 2% |
| Poland | 1 | 2% |
| Unknown | 52 | 87% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 12 | 20% |
| Researcher | 10 | 17% |
| Student > Master | 8 | 13% |
| Lecturer | 7 | 12% |
| Other | 6 | 10% |
| Other | 13 | 22% |
| Unknown | 4 | 7% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 28 | 47% |
| Biochemistry, Genetics and Molecular Biology | 5 | 8% |
| Engineering | 5 | 8% |
| Agricultural and Biological Sciences | 5 | 8% |
| Social Sciences | 4 | 7% |
| Other | 8 | 13% |
| Unknown | 5 | 8% |