Tree-Based Learning of Regulatory Network Topologies and Dynamics with Jump3.
Methods Mol Biol
; 1883: 217-233, 2019.
Article
em En
| MEDLINE
| ID: mdl-30547402
ABSTRACT
Inference of gene regulatory networks (GRNs) from time series data is a well-established field in computational systems biology. Most approaches can be broadly divided in two families model-based and model-free methods. These two families are highly complementary model-based methods seek to identify a formal mathematical model of the system. They thus have transparent and interpretable semantics but rely on strong assumptions and are rather computationally intensive. On the other hand, model-free methods have typically good scalability. Since they are not based on any parametric model, they are more flexible than model-based methods, but also less interpretable.In this chapter, we describe Jump3, a hybrid approach that bridges the gap between model-free and model-based methods. Jump3 uses a formal stochastic differential equation to model each gene expression but reconstructs the GRN topology with a nonparametric method based on decision trees. We briefly review the theoretical and algorithmic foundations of Jump3, and then proceed to provide a step-by-step tutorial of the associated software usage.
Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Árvores de Decisões
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Biologia de Sistemas
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Redes Reguladoras de Genes
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Aprendizado de Máquina
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Modelos Genéticos
Tipo de estudo:
Prognostic_studies
Idioma:
En
Revista:
Methods Mol Biol
Ano de publicação:
2019
Tipo de documento:
Article