Title |
Using graph theory to analyze biological networks
|
---|---|
Published in |
BioData Mining, April 2011
|
DOI | 10.1186/1756-0381-4-10 |
Pubmed ID | |
Authors |
Georgios A Pavlopoulos, Maria Secrier, Charalampos N Moschopoulos, Theodoros G Soldatos, Sophia Kossida, Jan Aerts, Reinhard Schneider, Pantelis G Bagos |
Abstract |
Understanding complex systems often requires a bottom-up analysis towards a systems biology approach. The need to investigate a system, not only as individual components but as a whole, emerges. This can be done by examining the elementary constituents individually and then how these are connected. The myriad components of a system and their interactions are best characterized as networks and they are mainly represented as graphs where thousands of nodes are connected with thousands of vertices. In this article we demonstrate approaches, models and methods from the graph theory universe and we discuss ways in which they can be used to reveal hidden properties and features of a network. This network profiling combined with knowledge extraction will help us to better understand the biological significance of the system. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 2 | 14% |
United Kingdom | 1 | 7% |
Korea, Republic of | 1 | 7% |
Belgium | 1 | 7% |
Unknown | 9 | 64% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 12 | 86% |
Practitioners (doctors, other healthcare professionals) | 1 | 7% |
Scientists | 1 | 7% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 13 | 1% |
Germany | 13 | 1% |
Brazil | 10 | <1% |
India | 8 | <1% |
France | 6 | <1% |
Spain | 5 | <1% |
United Kingdom | 4 | <1% |
Italy | 3 | <1% |
Belgium | 3 | <1% |
Other | 19 | 2% |
Unknown | 1129 | 93% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 299 | 25% |
Researcher | 202 | 17% |
Student > Master | 191 | 16% |
Student > Bachelor | 126 | 10% |
Student > Doctoral Student | 58 | 5% |
Other | 181 | 15% |
Unknown | 156 | 13% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 331 | 27% |
Biochemistry, Genetics and Molecular Biology | 196 | 16% |
Computer Science | 190 | 16% |
Engineering | 65 | 5% |
Mathematics | 42 | 3% |
Other | 184 | 15% |
Unknown | 205 | 17% |