Generative Learning of Dynamic Structures using Spanning Arborescence Sets
| Autor: | Coutant, Anthony, Rouveirol, Céline |
|---|---|
| Přispěvatelé: | Coutant, Anthony, Laboratoire d'Informatique de Paris-Nord (LIPN), Université Sorbonne Paris Cité (USPC)-Institut Galilée-Université Paris 13 (UP13)-Centre National de la Recherche Scientifique (CNRS) |
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
[INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG]
[INFO.INFO-DC] Computer Science [cs]/Distributed Parallel and Cluster Computing [cs.DC] [INFO.INFO-LG] Computer Science [cs]/Machine Learning [cs.LG] [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM] [INFO.INFO-DC]Computer Science [cs]/Distributed Parallel and Cluster Computing [cs.DC] [INFO.INFO-BI] Computer Science [cs]/Bioinformatics [q-bio.QM] |
| Popis: | Motivation: We focus on the problem of learning generative Gene Regulatory Network structures from scarce gene expression time series, where the (#variables/#individuals) ratio is high. Results: We propose the ELSA method computing a composite model using Bayesian Model Averaging from optimal spanning arborescences built from perturbed versions of the original dataset. We introduce various strategies to build composite from component models, including the use of both high and low ranked model traits to discriminate models, and validate them on the recent DREAM D8C1 challenge. |
| Databáze: | OpenAIRE |
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