Gene Regulatory Network Inferences Using a Maximum-Relevance and Maximum-Significance Strategy.

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Inference of time-delayed gene regulatory networks based on dynamic Bayesian network hybrid learning method.

P2860

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P2860

Gene Regulatory Network Inferences Using a Maximum-Relevance and Maximum-Significance Strategy.

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2016 nî lūn-bûn

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2016年論文

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2016年論文

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2016年论文

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Gene Regulatory Network Infere ...... Maximum-Significance Strategy.

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Gene Regulatory Network Infere ...... Maximum-Significance Strategy.

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Gene Regulatory Network Infere ...... Maximum-Significance Strategy.

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Gene Regulatory Network Infere ...... Maximum-Significance Strategy.

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Gene Regulatory Network Infere ...... Maximum-Significance Strategy.

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Gene Regulatory Network Infere ...... Maximum-Significance Strategy

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P2093

Bo Liao

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Wei Liu

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Wen Zhu

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P2860

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A comparison of learning algorithms for Bayesian networks: a case study based on data from an emergency medical service.

Inferring cellular networks--a review.

Optimization in computational systems biology.

Inferring gene regulatory networks by thermodynamic modeling.

Learning transcriptional regulatory networks from high throughput gene expression data using continuous three-way mutual information.

Computational methods for discovering gene networks from expression data.

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e0166115

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scholarly article

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10.1371/JOURNAL.PONE.0166115

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English

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Xiangtao Chen

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2016-11-09T00:00:00Z

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27829000

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5102470

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Gene Regulatory Network Inferences Using a Maximum-Relevance and Maximum-Significance Strategy.

about

P2860

P2860

Gene Regulatory Network Inferences Using a Maximum-Relevance and Maximum-Significance Strategy.

description

name

type

label

prefLabel

P1433

P1476

P2093

P2860

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P577

P698

P932

P356

P1433

P1476

P2093

P2860

P304

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P407

P433

P478

P50

P577

P698

P932