Gene-set approach for expression pattern analysis

By Dougu Nam, Seon-Young Kim. Briefings Bioinform., 2008

375 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/17a1420507630c2b07e0bcaf55783636f4b20e54

How researchers see this paper

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In Artificial Intelligence

1. Li Zhan
2. Tingze Feng
3. Gary D. Bader
4. Meijun Chen
5. Lang Zhou
6. Andrew Emili
7. Xiaochen Bo
8. Christoph Bock

In Molecular Biology

1. Intawat Nookaew
2. Li Zhan
3. Meijun Chen
4. Wenli Tang
5. Tianzhi Wu
6. Erqiang Hu
7. Zehan Dai
8. Shuangbing Xu

Citations in the corpus

• GSVA: gene set variation analysis for microarray and RNA-Seq data (2013, background): “Many other supervised, population based approaches have also been described [12,17,20,31-34].”
• clusterProfiler 4.0: A universal enrichment tool for interpreting omics data (2021, background): “, fungi,(2) plants(3)) embedded with particular annotations such as Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG).(4) Non-model organisms and functional annotations other than GO and KEGG are poorly supported.”
• Enriching the gene set analysis of genome-wide data by incorporating directionality of gene expression and combining statistical hypotheses and methods (2013, background): “Kim and Volsky (30) developed parametric analysis of gene set enrichment (PAGE) as an alternative to the popular GSEA.”
• Enrichment Map: A Network-Based Method for Gene-Set Enrichment Visualization and Interpretation (2010, methodology): “GSEA (Gene-Set Enrichment Analysis) [13], which utilizes the gene rank derived from differential expression or other statistics, is one of the most popular techniques in this group, though other whole-distribution testing models have been proposed [5,9].”
• GAGE: generally applicable gene set enrichment for pathway analysis (2009, neutral): “There are two categories of GSA based on the statistical tests used: sample randomization and gene randomization [8,15].”
• Analysing and interpreting DNA methylation data (2012, methodology): “Furthermore, researchers frequently apply gene set enrichment and pathway anal‑ ysis tools 113 to identify biologically meaningful trends in lists of DMRs, as shown by a recent study of shared DNA methylation patterns in ageing and in bladder cancer 114 .”