| Chapter title |
sbv IMPROVER: Modern Approach to Systems Biology
|
|---|---|
| Chapter number | 2 |
| Book title |
Biological Networks and Pathway Analysis
|
| Published in |
Methods in molecular biology, January 2017
|
| DOI | 10.1007/978-1-4939-7027-8_2 |
| Pubmed ID | |
| Book ISBNs |
978-1-4939-7025-4, 978-1-4939-7027-8
|
| Authors |
Svetlana Guryanova, Anna Guryanova |
| Abstract |
The increasing amount and variety of data in biosciences call for innovative methods of visualization, scientific verification, and pathway analysis. Novel approaches to biological networks and research quality control are important because of their role in development of new products, improvement, and acceleration of existing health policies and research for novel ways of solving scientific challenges. One such approach is sbv IMPROVER. It is a platform that uses crowdsourcing and verification to create biological networks with easy public access. It contains 120 networks built in Biological Expression Language (BEL) to interpret data from PubMed articles with high-quality verification available for free on the CBN database. Computable, human-readable biological networks with a structured syntax are a powerful way of representing biological information generated from high-density data. This article presents sbv IMPROVER, a crowd-verification approach for the visualization and expansion of biological networks. |
Mendeley readers
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 12 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Professor > Associate Professor | 3 | 25% |
| Researcher | 3 | 25% |
| Student > Master | 2 | 17% |
| Student > Ph. D. Student | 1 | 8% |
| Other | 1 | 8% |
| Other | 2 | 17% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 3 | 25% |
| Computer Science | 2 | 17% |
| Agricultural and Biological Sciences | 1 | 8% |
| Mathematics | 1 | 8% |
| Immunology and Microbiology | 1 | 8% |
| Other | 1 | 8% |
| Unknown | 3 | 25% |