Integration of probabilistic regulatory networks into constraint-based models of metabolism with applications to Alzheimer's disease.

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作者： Han,Yu ^[1] ; Rachael Hageman,Blair ^[2]

作者单位： State University of New York at Buffalo, 3435 Main Street, Buffalo, 14214, US. ^[1] State University of New York at Buffalo, 3435 Main Street, Buffalo, 14214, US. hageman@buffalo.edu. ^[2]

关键词 Bayesian network Belief propagation Constraint-based model Gene network Metabolism Model integration Probabilistic reasoning

主题词阿尔茨海默病(Alzheimer Disease);贝叶斯定理(Bayes Theorem);酶类(Enzymes);基因调控网络(Gene Regulatory Networks);人类(Humans);代谢网络和途径(Metabolic Networks and Pathways);模型, 生物学(Models, Biological);模型, 统计学(Models, Statistical);表型(Phenotype);信号传导(Signal Transduction)

DOI 10.1186/s12859-019-2872-8

PMID 31291905

发布时间 2020-02-25

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BMC bioinformatics

2019年20卷1期

386页

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Integration of probabilistic regulatory networks into constraint-based models of metabolism with applications to Alzheimer's disease.

BMC bioinformatics

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BMC bioinformatics

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Integration of probabilistic regulatory networks into constraint-based models of metabolism with applications to Alzheimer's disease.

BMC bioinformatics

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BMC bioinformatics

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