State Space Model with hidden variables for reconstruction of gene regulatory networks

BACKGROUND: State Space Model (SSM) is a relatively new approach to inferring gene regulatory networks. It requires less computational time than Dynamic Bayesian Networks (DBN). There are two types of variables in the linear SSM, observed variables and hidden variables. SSM uses an iterative method,...

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Detalles Bibliográficos
Autores principales: Wu, Xi, Li, Peng, Wang, Nan, Gong, Ping, Perkins, Edward J, Deng, Youping, Zhang, Chaoyang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2011
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3287571/
https://www.ncbi.nlm.nih.gov/pubmed/22784622
http://dx.doi.org/10.1186/1752-0509-5-S3-S3

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3287571/
https://www.ncbi.nlm.nih.gov/pubmed/22784622
http://dx.doi.org/10.1186/1752-0509-5-S3-S3

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