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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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書誌詳細
主要な著者: Wu, Xi, Li, Peng, Wang, Nan, Gong, Ping, Perkins, Edward J, Deng, Youping, Zhang, Chaoyang
フォーマット: Artigo
言語:Inglês
出版事項: BioMed Central 2011
主題:
オンライン・アクセス: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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