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Papers: 10.3390/cancers13081768

https://doi.org/10.3390/cancers13081768

Systematic Analysis of the Transcriptome Profiles and Co-Expression Networks of Tumour Endothelial Cells Identifies Several Tumour-Associated Modules and Potential Therapeutic Targets in Hepatocellular Carcinoma

Cited by: 8
Author(s): Thomas Mohr, Sonja Katz, Verena Paulitschke, Nadim Aizarani, Alexander Tolios
Published: over 5 years ago

Software Mentions 9

bioconductor: affy
Methods for Affymetrix Oligonucleotide Arrays
Papers that mentioned: 989
Very Likely Science (98)
bioconductor: arrayQualityMetrics
Quality metrics report for microarray data sets
Papers that mentioned: 190
Very Likely Science (100)
bioconductor: genefilter
genefilter: methods for filtering genes from high-throughput experiments
Papers that mentioned: 142
Very Likely Science (75)
bioconductor: GOSemSim
GO-terms Semantic Similarity Measures
Papers that mentioned: 118
Very Likely Science (90)
bioconductor: GSVA
Gene Set Variation Analysis for microarray and RNA-seq data
Papers that mentioned: 893
Very Likely Science (100)
bioconductor: M3C
Monte Carlo Reference-based Consensus Clustering
Papers that mentioned: 7
Very Likely Science (90)
cran: fpc
Flexible Procedures for Clustering
Papers that mentioned: 99
Very Likely Science (75)
cran: WGCNA
Weighted Correlation Network Analysis
Papers that mentioned: 3,479
Very Likely Science (85)
pypi: GSVA
Python CLI and module for running the GSVA R bioconductor package with Python Pandas inputs and outputs.
Papers that mentioned: 893
Very Likely Science (100)