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NetPathMiner: R/Bioconductor package for network path mining through gene expression.
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PanViz: interactive visualization of the structure of functionally annotated pangenomes.
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rBiopaxParser--an R package to parse, modify and visualize BioPAX data.
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GenVisR: Genomic Visualizations in R.
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lpNet: a linear programming approach to reconstruct signal transduction networks.
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LedPred: an R/bioconductor package to predict regulatory sequences using support vector machines.
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Pathview: an R/Bioconductor package for pathway-based data integration and visualization.
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COPS: A novel platform for multi-omic disease subtype discovery via robust multi-objective evaluation of clustering algorithms.
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Robustness evaluations of pathway activity inference methods on gene expression data.
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CTpathway: a CrossTalk-based pathway enrichment analysis method for cancer research.
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Analysing the meta-interaction between pathways by gene set topological impact analysis.
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GeneSetCluster: a tool for summarizing and integrating gene-set analysis results.
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Concurrent lipidomics and proteomics on malignant plasma cells from multiple myeloma patients: Probing the lipid metabolome.
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本文引用的文献

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RCytoscape: tools for exploratory network analysis.
BMC Bioinformatics. 2013 Jul 9;14:217. doi: 10.1186/1471-2105-14-217.
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Pathview: an R/Bioconductor package for pathway-based data integration and visualization.
Bioinformatics. 2013 Jul 15;29(14):1830-1. doi: 10.1093/bioinformatics/btt285. Epub 2013 Jun 4.
3
rBiopaxParser--an R package to parse, modify and visualize BioPAX data.
Bioinformatics. 2013 Feb 15;29(4):520-2. doi: 10.1093/bioinformatics/bts710. Epub 2012 Dec 28.
4
Identifying neighborhoods of coordinated gene expression and metabolite profiles.
PLoS One. 2012;7(2):e31345. doi: 10.1371/journal.pone.0031345. Epub 2012 Feb 15.
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Mining metabolic pathways through gene expression.
Bioinformatics. 2010 Sep 1;26(17):2128-35. doi: 10.1093/bioinformatics/btq344. Epub 2010 Jun 29.
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Extracting active pathways from gene expression data.
Bioinformatics. 2003 Oct;19 Suppl 2:ii238-44. doi: 10.1093/bioinformatics/btg1084.

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