| Chapter title |
Mining biological networks from full-text articles.
|
|---|---|
| Chapter number | 8 |
| Book title |
Biomedical Literature Mining
|
| Published in |
Methods in molecular biology, May 2014
|
| DOI | 10.1007/978-1-4939-0709-0_8 |
| Pubmed ID | |
| Book ISBNs |
978-1-4939-0708-3, 978-1-4939-0709-0
|
| Authors |
Czarnecki, J., Shepherd, Adrian J., Jan Czarnecki, Adrian J. Shepherd, Czarnecki, Jan |
| Abstract |
The study of biological networks is playing an increasingly important role in the life sciences. Many different kinds of biological system can be modelled as networks; perhaps the most important examples are protein-protein interaction (PPI) networks, metabolic pathways, gene regulatory networks, and signalling networks. Although much useful information is easily accessible in publicly databases, a lot of extra relevant data lies scattered in numerous published papers. Hence there is a pressing need for automated text-mining methods capable of extracting such information from full-text articles. Here we present practical guidelines for constructing a text-mining pipeline from existing code and software components capable of extracting PPI networks from full-text articles. This approach can be adapted to tackle other types of biological network. |
X Demographics
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 100% |
Mendeley readers
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Spain | 1 | 14% |
| Unknown | 6 | 86% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 3 | 43% |
| Researcher | 2 | 29% |
| Professor | 1 | 14% |
| Student > Master | 1 | 14% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 3 | 43% |
| Agricultural and Biological Sciences | 1 | 14% |
| Unknown | 3 | 43% |