Name
Title | ||
|---|---|---|
Dynamic Probabilistic Threshold Networks to Infer Signaling Pathways from Time-Course Perturbation Data. |
Abstract | ||
|---|---|---|
Network inference deals with the reconstruction of molecular networks from experimental data. Given N molecular species, the challenge is to find the underlying network. Due to data limitations, this typically is an ill-posed problem, and requires the integration of prior biological knowledge or strong regularization. We here focus on the situation when time-resolved measurements of a system’s response after systematic perturbations are available. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1186/1471-2105-15-250 | BMC Bioinformatics |
Keywords | Field | DocType |
algorithms,microarrays,bioinformatics,systems biology,monte carlo method,markov chains,signal transduction,bayes theorem | Markov chain Monte Carlo,Inference,Computer science,Markov chain,Algorithm,Systems biology,Regularization (mathematics),Bayesian network,Bioinformatics,Probabilistic logic,Bayes' theorem | Journal |
Volume | Issue | ISSN |
15 | 1 | 1471-2105 |
Citations | PageRank | References |
8 | 0.50 | 31 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Narsis A. Kiani | 1 | 76 | 9.98 |
| Lars Kaderali | 2 | 161 | 16.32 |