Index of papers in PLOS Comp. Biol. that mention
  • enrichment analysis
Vesna Memišević, Nela Zavaljevski, Seesandra V. Rajagopala, Keehwan Kwon, Rembert Pieper, David DeShazer, Jaques Reifman, Anders Wallqvist
Characteristics of host proteins interacting with known B. mal/ei virulence factors
First, we applied functional enrichment analyses based on Gene Ontology (GO) annotation data [23] to assess the characteristics of the human proteins targeted by the nine virulence factors.
Gene set functional enrichment analyses
Gene set functional enrichment analyses
Gene set functional enrichment analyses
We performed GO and KEGG enrichment analyses in R using the Bioconductor packages Bio-Mart and KEGGgraph, respectively [43, 44].
Gene set functional enrichment analyses
For the KEGG enrichment analysis , as the universe of human proteins, we used the human proteins available in KEGGgraph that participated in at least one KEGG pathway [44].
Using multiple host-pathogen interaction networks to predict the role of pathogen proteins
However, detecting specific mechanisms of action for each pathogen protein based on enrichment analysis of large-scale Y2H protein interaction data is not trivial.
enrichment analysis is mentioned in 8 sentences in this paper.
Topics mentioned in this paper:
Christopher R. S. Banerji, Simone Severini, Carlos Caldas, Andrew E. Teschendorff
Supporting Information
Genes utilised in the gene set enrichment analysis to identify gene sets associated with signalling entropy’s prognostic power in breast and lung cancer.
Supporting Information
Gene set enrichment analysis results displaying the top 10 most significant enriched gene sets associated with signalling entropy’s prognostic power in breast and lung cancer.
Supporting Information
Tables display results for the gene set enrichment analysis performed on gene lists identified in lung and breast cancer separately, both With and Without the intersection of the
The prognostic impact of signalling entropy is associated with genes involved in cancer stem cells and treatment resistance
We performed a gene set enrichment analysis , using a Fisher’s Exact test, comparing each of these gene lists separately against the Molecular Signatures Database [50] (S6 Table shows the top 10 enriched gene sets for both gene lists).
The prognostic impact of signalling entropy is associated with genes involved in cancer stem cells and treatment resistance
We note that gene set enrichment analysis performed on the genes comprising the SE scores gave broadly similar results (S7 Table).
enrichment analysis is mentioned in 6 sentences in this paper.
Topics mentioned in this paper:
Christopher DeBoever, Emanuela M. Ghia, Peter J. Shepard, Laura Rassenti, Christian L. Barrett, Kristen Jepsen, Catriona H. M. Jamieson, Dennis Carson, Thomas J. Kipps, Kelly A. Frazer
Cryptic 3’SS selection is limited to tumors with mutations in HEAT repeat hotspots
To characterize the roles of the genes affected by cryptic 3’SS usage, we performed a gene set enrichment analysis for the 912 genes that contained the 619 proximal and 417 distal cryptic 3’SSs used significantly more often in the SF3BI mutant samples (SS File).
Cryptic 3’SS selection is limited to tumors with mutations in HEAT repeat hotspots
These results may reflect the fact that we are more likely to identify cryptic 3’SSs in genes that are highly expressed which may bias such a gene set enrichment analysis .
Cryptic 3’SSs are used infrequently relative to canonical 3’SSs
To investigate the potential role of NMD, we identified differentially expressed genes between the SF3BI mutant and wild-type samples in a joint analysis of all three cancers and performed a gene set enrichment analysis .
Differential gene expression
Gene set enrichment analysis was performed using GSEA [21].
Gene set enrichment for genes with cryptic 3’SS usage
We performed a gene set enrichment analysis using GSEA [21] for the genes that contained cryptic 3’SSs by combining the genes that contained the 619 proximal (S3 File) and the 417 dis
enrichment analysis is mentioned in 5 sentences in this paper.
Topics mentioned in this paper:
Minseung Kim, Violeta Zorraquino, Ilias Tagkopoulos
Biomarker discovery through functional and network analysis
Functional enrichment analysis of the most informative genes reveals a rich repertoire of biological processes where their differential enrichment is discriminative of each specific class (Fig.
Selection of most informative genes and functional enrichment analysis
Selection of most informative genes and functional enrichment analysis
Selection of most informative genes and functional enrichment analysis
For functional enrichment analysis , we use all selected genes that optimize the classifier performance.
enrichment analysis is mentioned in 3 sentences in this paper.
Topics mentioned in this paper: