(Q33588476)

28 July 2017

title

Reverse engineering large-scale genetic networks: synthetic versus real data (English)

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28 July 2017

Luwen Zhang

1

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28 July 2017

Mei Xiao

series ordinal

2

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28 July 2017

Yong Wang

series ordinal

3

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28 July 2017

Wu Zhang

series ordinal

4

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28 July 2017

publication date

1 April 2010

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28 July 2017

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28 July 2017

volume

89

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28 July 2017

issue

1

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28 July 2017

page(s)

73-80

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Inferring transcriptional compensation interactions in yeast via stepwise structure equation modeling

28 July 2017

cites work

1 reference

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https://api.crossref.org/works/10.1007%2FS12041-010-0013-2

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Comparing association network algorithms for reverse engineering of large-scale gene regulatory networks: synthetic versus real data

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Importance of input perturbations and stochastic gene expression in the reverse engineering of genetic regulatory networks: insights from an identifiability analysis of an in silico network

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A Bayesian approach to reconstructing genetic regulatory networks with hidden factors

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Reverse engineering of regulatory networks in human B cells

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Reverse engineering gene networks using singular value decomposition and robust regression

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Reverse-engineering transcription control networks.

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Topology of Evolving Networks: Local Events and Universality

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Developmental regulation of glutamate receptor field size by nonvesicular glutamate release

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Reveal, a general reverse engineering algorithm for inference of genetic network architectures.

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Wrestling with pleiotropy: genomic and topological analysis of the yeast gene expression network.

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Collective dynamics of 'small-world' networks

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Theory and limitations of genetic network inference from microarray data

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Assessment of survival prediction models based on microarray data

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10.1007/S12041-010-0013-2

Identifiers

DOI

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Dimensions Publication ID

28 July 2017

1038831481

0 references

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