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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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Bibliografiske detaljer
Main Authors: Wu, Xi, Li, Peng, Wang, Nan, Gong, Ping, Perkins, Edward J, Deng, Youping, Zhang, Chaoyang
Format: Artigo
Sprog:Inglês
Udgivet: BioMed Central 2011
Fag:
Online adgang:https://ncbi.nlm.nih.gov/pmc/articles/PMC3287571/
https://ncbi.nlm.nih.gov/pubmed/22784622
https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1186/1752-0509-5-S3-S3
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