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- W2018462761 abstract "The gene regulation network (GRN) is a high-dimensional complex system, which can be represented by various mathematical or statistical models. The ordinary differential equation (ODE) model is one of the popular dynamic GRN models. High-dimensional linear ODE models have been proposed to identify GRNs, but with a limitation of the linear regulation effect assumption. In this article, we propose a sparse additive ODE (SA-ODE) model, coupled with ODE estimation methods and adaptive group LASSO techniques, to model dynamic GRNs that could flexibly deal with nonlinear regulation effects. The asymptotic properties of the proposed method are established and simulation studies are performed to validate the proposed approach. An application example for identifying the nonlinear dynamic GRN of T-cell activation is used to illustrate the usefulness of the proposed method." @default.
- W2018462761 created "2016-06-24" @default.
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- W2018462761 creator A5050075582 @default.
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- W2018462761 date "2014-04-03" @default.
- W2018462761 modified "2023-09-23" @default.
- W2018462761 title "Sparse Additive Ordinary Differential Equations for Dynamic Gene Regulatory Network Modeling" @default.
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- W2018462761 doi "https://doi.org/10.1080/01621459.2013.859617" @default.
- W2018462761 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/4104722" @default.
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