Name
Title | ||
|---|---|---|
PRODIGEN: visualizing the probability landscape of stochastic gene regulatory networks in state and time space. |
Abstract | ||
|---|---|---|
We demonstrate the effectiveness of the tool through two case studies on the computed probabilistic landscape of a gene regulatory network and of a toggle-switch network. Domain expert feedback indicates that our visual approach can help biologists: 1) visualize probabilities of stable states, 2) explore the temporal probability distributions, and 3) discover small peaks in the probability landscape that have potential relation to specific diseases. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1186/s12859-016-1447-1 | BMC Bioinformatics |
Keywords | Field | DocType |
Feature detection and tracking,Modelling/Simulation,Network analysis,Systems biology | Computer science,Systems biology,Stochastic process,Bioinformatics,Gene regulatory network | Journal |
Volume | Issue | ISSN |
18 | S-2 | 1471-2105 |
Citations | PageRank | References |
2 | 0.36 | 13 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chihua Ma | 1 | 8 | 3.10 |
| Timothy Luciani | 2 | 15 | 4.19 |
| Anna Terebus | 3 | 5 | 1.11 |
| Jie Liang | 4 | 239 | 33.55 |
| G Elisabeta Marai | 5 | 136 | 20.43 |