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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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| Hoofdauteurs: | , , , , , , |
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| Formaat: | Artigo |
| Taal: | Inglês |
| Gepubliceerd in: |
BioMed Central
2011
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| Onderwerpen: | |
| Online toegang: | 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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