Name
Abstract | ||
|---|---|---|
The definition of general topological principles allowing for graph characterization is an important pre-requisite for investigating structure–function relationships in biological networks. Here we approached the problem by means of an explorative, data-driven strategy, building upon a size-balanced data set made of around 200 distinct biological networks from seven functional classes and simulated networks coming from three mathematical graph models. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1016/j.biosystems.2016.01.004 | Biosystems |
Keywords | Field | DocType |
Graph theory,Systems biology,Topology,Biocomplexity,Principal component analysis | Graph theory,Network motif,Biological network,Geometric networks,Network architecture,Systems biology,Artificial intelligence,Complex network,Mathematics,Machine learning,Test set | Journal |
Volume | ISSN | Citations |
141 | 0303-2647 | 2 |
PageRank | References | Authors |
0.38 | 9 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Havva Kohestani | 1 | 2 | 0.38 |
| Alessandro Giuliani | 2 | 170 | 25.21 |