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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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| Main Authors: | , , , , , , |
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| Formato: | Artigo |
| Idioma: | Inglês |
| Publicado em: |
BioMed Central
2011
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| Assuntos: | |
| Acesso em linha: | 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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