| Chapter title |
Cytoscape: software for visualization and analysis of biological networks.
|
|---|---|
| Chapter number | 18 |
| Book title |
Data Mining in Proteomics
|
| Published in |
Methods in molecular biology, November 2010
|
| DOI | 10.1007/978-1-60761-987-1_18 |
| Pubmed ID | |
| Book ISBNs |
978-1-60761-986-4, 978-1-60761-987-1
|
| Authors |
Kohl M, Wiese S, Warscheid B, Michael Kohl, Sebastian Wiese, Bettina Warscheid, Kohl, Michael, Wiese, Sebastian, Warscheid, Bettina |
| Abstract |
Substantial progress has been made in the field of "omics" research (e.g., Genomics, Transcriptomics, Proteomics, and Metabolomics), leading to a vast amount of biological data. In order to represent large biological data sets in an easily interpretable manner, this information is frequently visualized as graphs, i.e., a set of nodes and edges. Nodes are representations of biological molecules and edges connect the nodes depicting some kind of relationship. Obviously, there is a high demand for computer-based assistance for both visualization and analysis of biological data, which are often heterogeneous and retrieved from different sources. This chapter focuses on software tools that assist in visual exploration and analysis of biological networks. Global requirements for such programs are discussed. Utilization of visualization software is exemplified using the widely used Cytoscape tool. Additional information about the use of Cytoscape is provided in the Notes section. Furthermore, special features of alternative software tools are highlighted in order to assist researchers in the choice of an adequate program for their specific requirements. |
Mendeley readers
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 3 | 1% |
| United States | 2 | <1% |
| Spain | 2 | <1% |
| South Africa | 2 | <1% |
| France | 1 | <1% |
| Italy | 1 | <1% |
| Chile | 1 | <1% |
| Colombia | 1 | <1% |
| Netherlands | 1 | <1% |
| Other | 1 | <1% |
| Unknown | 252 | 94% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 52 | 19% |
| Researcher | 28 | 10% |
| Student > Master | 24 | 9% |
| Student > Bachelor | 23 | 9% |
| Other | 10 | 4% |
| Other | 44 | 16% |
| Unknown | 86 | 32% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 55 | 21% |
| Biochemistry, Genetics and Molecular Biology | 54 | 20% |
| Computer Science | 11 | 4% |
| Medicine and Dentistry | 10 | 4% |
| Pharmacology, Toxicology and Pharmaceutical Science | 7 | 3% |
| Other | 32 | 12% |
| Unknown | 98 | 37% |