Comparative assessment of differential network analysis methods.

about

Gene co-expression analysis for functional classification and gene-disease predictions.Incorporating prior information into differential network analysis using non-paranormal graphical models.xMWAS: a data-driven integration and differential network analysis tool.

P2860

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P2860

Comparative assessment of differential network analysis methods.

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http://www.wikidata.org/entity/Q39544929

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2016 nî lūn-bûn

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2016年の論文

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2016年論文

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2016年論文

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2016年論文

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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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2016年论文

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2016年论文

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name

Comparative assessment of differential network analysis methods.

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Comparative assessment of differential network analysis methods.

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Comparative assessment of differential network analysis methods.

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Comparative assessment of differential network analysis methods.

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Comparative assessment of differential network analysis methods.

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Comparative assessment of differential network analysis methods.

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P1476

Comparative assessment of differential network analysis methods.

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P2093

Berit Haldemann

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Dido Lenze

http://www.w3.org/2001/XMLSchema#string

Karin Zimmermann

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Ulf Leser

http://www.w3.org/2001/XMLSchema#string

Yvonne Lichtblau

http://www.w3.org/2001/XMLSchema#string

P2860

Differential expression analysis for sequence count data

WGCNA: an R package for weighted correlation network analysis

Human MicroRNA targets

Cancer genome landscapes

Ensembl 2014

miRTarBase: a database curates experimentally validated microRNA-target interactions

ORegAnno: an open-access community-driven resource for regulatory annotation

miR2Disease: a manually curated database for microRNA deregulation in human disease

MicroRNA-mediated feedback and feedforward loops are recurrent network motifs in mammals

High-betweenness proteins in the yeast protein interaction network

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scholarly article

P356

10.1093/BIB/BBW061

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P50

Michael Hummel

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2016-07-29T00:00:00Z

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P698

27473063

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Comparative assessment of differential network analysis methods.

about

P2860

P2860

Comparative assessment of differential network analysis methods.

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