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Artykuły w czasopismach na temat „Gene set enrichment analyses”

Autor: Grafiati

Data publikacji: 3 czerwca 2025

Data aktualizacji: 31 lipca 2025

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Alexeyenko, Andrey, Woojoo Lee, Maria Pernemalm, et al. "Network enrichment analysis: extension of gene-set enrichment analysis to gene networks." BMC Bioinformatics 13, no. 1 (2012): 226. http://dx.doi.org/10.1186/1471-2105-13-226.

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Irizarry, Rafael A., Chi Wang, Yun Zhou, and Terence P. Speed. "Gene set enrichment analysis made simple." Statistical Methods in Medical Research 18, no. 6 (2009): 565–75. http://dx.doi.org/10.1177/0962280209351908.

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Backes, C., A. Keller, J. Kuentzer, et al. "GeneTrail--advanced gene set enrichment analysis." Nucleic Acids Research 35, Web Server (2007): W186—W192. http://dx.doi.org/10.1093/nar/gkm323.

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Saxena, Vishal, Dennis Orgill, and Isaac Kohane. "Absolute enrichment: gene set enrichment analysis for homeostatic systems." Nucleic Acids Research 34, no. 22 (2006): e151-e151. http://dx.doi.org/10.1093/nar/gkl766.

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Tragante, Vinicius, Johannes M. I. H. Gho, Janine F. Felix, et al. "Gene Set Enrichment Analyses: lessons learned from the heart failure phenotype." BioData Mining 10, no. 1 (2017): 18. https://doi.org/10.1186/s13040-017-0137-5.

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<strong>Background: </strong>Genetic studies for complex diseases have predominantly discovered main effects at individual loci, but have not focused on genomic and environmental contexts important for a phenotype. Gene Set Enrichment Analysis (GSEA) aims to address this by identifying sets of genes or biological pathways contributing to a phenotype, through gene-gene interactions or other mechanisms, which are not the focus of conventional association methods.<strong>Results: </strong>Approaches that utilize GSEA can now take input from array chips, either gene-centric or genome-wide, but are

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Tan, Yan, Felix Wu, Pablo Tamayo, W. Nicholas Haining, and Jill P. Mesirov. "Constellation Map: Downstream visualization and interpretation of gene set enrichment results." F1000Research 4 (June 24, 2015): 167. http://dx.doi.org/10.12688/f1000research.6644.1.

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Summary: Gene set enrichment analysis (GSEA) approaches are widely used to identify coordinately regulated genes associated with phenotypes of interest. Here, we present Constellation Map, a tool to visualize and interpret the results when enrichment analyses yield a long list of significantly enriched gene sets. Constellation Map identifies commonalities that explain the enrichment of multiple top-scoring gene sets and maps the relationships between them. Constellation Map can help investigators take full advantage of GSEA and facilitates the biological interpretation of enrichment results. A

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Hukku, Abhay, Corbin Quick, Francesca Luca, Roger Pique-Regi, and Xiaoquan Wen. "BAGSE: a Bayesian hierarchical model approach for gene set enrichment analysis." Bioinformatics 36, no. 6 (2019): 1689–95. http://dx.doi.org/10.1093/bioinformatics/btz831.

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Abstract Motivation Gene set enrichment analysis has been shown to be effective in identifying relevant biological pathways underlying complex diseases. Existing approaches lack the ability to quantify the enrichment levels accurately, hence preventing the enrichment information to be further utilized in both upstream and downstream analyses. A modernized and rigorous approach for gene set enrichment analysis that emphasizes both hypothesis testing and enrichment estimation is much needed. Results We propose a novel computational method, Bayesian Analysis of Gene Set Enrichment (BAGSE), for ge

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Prummer, Michael. "Enhancing gene set enrichment using networks." F1000Research 8 (January 30, 2019): 129. http://dx.doi.org/10.12688/f1000research.17824.1.

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Differential gene expression (DGE) studies often suffer from poor interpretability of their primary results, i.e., thousands of differentially expressed genes. This has led to the introduction of gene set analysis (GSA) methods that aim at identifying interpretable global effects by grouping genes into sets of common context, such as, molecular pathways, biological function or tissue localization. In practice, GSA often results in hundreds of differentially regulated gene sets. Similar to the genes they contain, gene sets are often regulated in a correlative fashion because they share many of

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Prummer, Michael. "Enhancing gene set enrichment using networks." F1000Research 8 (July 16, 2019): 129. http://dx.doi.org/10.12688/f1000research.17824.2.

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Differential gene expression (DGE) studies often suffer from poor interpretability of their primary results, i.e., thousands of differentially expressed genes. This has led to the introduction of gene set analysis (GSA) methods that aim at identifying interpretable global effects by grouping genes into sets of common context, such as, molecular pathways, biological function or tissue localization. In practice, GSA often results in hundreds of differentially regulated gene sets. Similar to the genes they contain, gene sets are often regulated in a correlative fashion because they share many of

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Schmid, Florian, Matthias Schmid, Christoph Müssel, et al. "GiANT: gene set uncertainty in enrichment analysis." Bioinformatics 32, no. 12 (2016): 1891–94. http://dx.doi.org/10.1093/bioinformatics/btw030.

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Glaab, Enrico, Anaïs Baudot, Natalio Krasnogor, Reinhard Schneider, and Alfonso Valencia. "EnrichNet: network-based gene set enrichment analysis." Bioinformatics 28, no. 18 (2012): i451—i457. http://dx.doi.org/10.1093/bioinformatics/bts389.

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Yang, Hong, Ying Shi, Anqi Lin, et al. "PESSA: A web tool for pathway enrichment score-based survival analysis in cancer." PLOS Computational Biology 20, no. 5 (2024): e1012024. http://dx.doi.org/10.1371/journal.pcbi.1012024.

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The activation levels of biologically significant gene sets are emerging tumor molecular markers and play an irreplaceable role in the tumor research field; however, web-based tools for prognostic analyses using it as a tumor molecular marker remain scarce. We developed a web-based tool PESSA for survival analysis using gene set activation levels. All data analyses were implemented via R. Activation levels of The Molecular Signatures Database (MSigDB) gene sets were assessed using the single sample gene set enrichment analysis (ssGSEA) method based on data from the Gene Expression Omnibus (GEO

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Fabris, Fabio, Daniel Palmer, João Pedro de Magalhães, and Alex A. Freitas. "Comparing enrichment analysis and machine learning for identifying gene properties that discriminate between gene classes." Briefings in Bioinformatics 21, no. 3 (2019): 803–14. http://dx.doi.org/10.1093/bib/bbz028.

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Abstract Biologists very often use enrichment methods based on statistical hypothesis tests to identify gene properties that are significantly over-represented in a given set of genes of interest, by comparison with a ‘background’ set of genes. These enrichment methods, although based on rigorous statistical foundations, are not always the best single option to identify patterns in biological data. In many cases, one can also use classification algorithms from the machine-learning field. Unlike enrichment methods, classification algorithms are designed to maximize measures of predictive perfor

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Cerulo, Luigi, and Stefano Maria Pagnotta. "massiveGST: A Mann–Whitney–Wilcoxon Gene-Set Test Tool That Gives Meaning to Gene-Set Enrichment Analysis." Entropy 24, no. 5 (2022): 739. http://dx.doi.org/10.3390/e24050739.

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Gene-set enrichment analysis is the key methodology for obtaining biological information from transcriptomic space’s statistical result. Since its introduction, Gene-set Enrichment analysis methods have obtained more reliable results and a wider range of application. Great attention has been devoted to global tests, in contrast to competitive methods that have been largely ignored, although they appear more flexible because they are independent from the source of gene-profiles. We analyzed the properties of the Mann–Whitney–Wilcoxon test, a competitive method, and adapted its interpretation in

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Shi, Jing, and Michael Walker. "Gene Set Enrichment Analysis (GSEA) for Interpreting Gene Expression Profiles." Current Bioinformatics 2, no. 2 (2007): 133–37. http://dx.doi.org/10.2174/157489307780618231.

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Cheng, Jack, Lee-Fen Hsu, Ying-Hsu Juan, Hsin-Ping Liu, and Wei-Yong Lin. "Pathway-targeting gene matrix for Drosophila gene set enrichment analysis." PLOS ONE 16, no. 10 (2021): e0259201. http://dx.doi.org/10.1371/journal.pone.0259201.

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Gene Set Enrichment Analysis (GSEA) is a powerful algorithm to determine biased pathways between groups based on expression profiling. However, for fruit fly, a popular animal model, gene matrixes for GSEA are unavailable. This study provides the pathway-targeting gene matrixes based on Reactome and KEGG database for fruit fly. An expression profiling containing neurons or glia of fruit fly was used to validate the feasibility of the generated gene matrixes. We validated the gene matrixes and identified characteristic neuronal and glial pathways, including mRNA splicing and endocytosis. In con

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Zhang, Wei, R. Stephanie Huang, Shiwei Duan, and M. Eileen Dolan. "Gene Set Enrichment Analyses Revealed Differences in Gene Expression Patterns between Males and Females." In Silico Biology 9, no. 3 (2009): 55–63. http://dx.doi.org/10.3233/isb-2009-0387.

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Chen, Zhongbo, Kuang Lin, Aleksey Shatunov, Ashley Jones, and Ammar Al-Chalabi. "GENE SET ENRICHMENT ANALYSIS IN AMYOTROPHIC LATERAL SCLEROSIS." Journal of Neurology, Neurosurgery & Psychiatry 86, no. 11 (2015): e4.57-e4. http://dx.doi.org/10.1136/jnnp-2015-312379.15.

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BackgroundAmyotrophic lateral sclerosis (ALS) is a devastating neurodegenerative disease. We examined the phylogenetics of ALS-susceptibility genes from our previously-published genome-wide association study (GWAS).MethodsWe examined the association between ALS-susceptibility genes and hybridisation from the Neanderthal genome using a modified gene set enrichment analysis (GSEA). These gene sets comprise thirteen candidate regions for Neanderthal to non-African human gene flow [UCSC Genome Browser]. We ranked the ascending p-values of 442,057 GWAS SNPs and simulated the distribution 100,000 ti

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Tsai, Chen-An, and Pei-Hsun Li. "Gene Set Enrichment Analysis in RNA-Seq Data." Journal of Data Science 18, no. 4 (2021): 632–48. http://dx.doi.org/10.6339/jds.202010_18(4).0003.

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Chien, Chih-Yi, Ching-Wei Chang, Chen-An Tsai, and James J. Chen. "MAVTgsa: An R Package for Gene Set (Enrichment) Analysis." BioMed Research International 2014 (2014): 1–11. http://dx.doi.org/10.1155/2014/346074.

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Gene set analysis methods aim to determine whether an a priori defined set of genes shows statistically significant difference in expression on either categorical or continuous outcomes. Although many methods for gene set analysis have been proposed, a systematic analysis tool for identification of different types of gene set significance modules has not been developed previously. This work presents an R package, called MAVTgsa, which includes three different methods for integrated gene set enrichment analysis. (1) The one-sided OLS (ordinary least squares) test detects coordinated changes of

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Kuleshov, Maxim V., Jennifer E. L. Diaz, Zachary N. Flamholz, et al. "modEnrichr: a suite of gene set enrichment analysis tools for model organisms." Nucleic Acids Research 47, W1 (2019): W183—W190. http://dx.doi.org/10.1093/nar/gkz347.

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Abstract High-throughput experiments produce increasingly large datasets that are difficult to analyze and integrate. While most data integration approaches focus on aligning metadata, data integration can be achieved by abstracting experimental results into gene sets. Such gene sets can be made available for reuse through gene set enrichment analysis tools such as Enrichr. Enrichr currently only supports gene sets compiled from human and mouse, limiting accessibility for investigators that study other model organisms. modEnrichr is an expansion of Enrichr for four model organisms: fish, fly,

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Tamayo, Pablo, George Steinhardt, Arthur Liberzon, and Jill P. Mesirov. "The limitations of simple gene set enrichment analysis assuming gene independence." Statistical Methods in Medical Research 25, no. 1 (2012): 472–87. http://dx.doi.org/10.1177/0962280212460441.

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Carlevaro-Fita, Joana, Leibo Liu, Yuan Zhou, et al. "LnCompare: gene set feature analysis for human long non-coding RNAs." Nucleic Acids Research 47, W1 (2019): W523—W529. http://dx.doi.org/10.1093/nar/gkz410.

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Abstract Interest in the biological roles of long noncoding RNAs (lncRNAs) has resulted in growing numbers of studies that produce large sets of candidate genes, for example, differentially expressed between two conditions. For sets of protein-coding genes, ontology and pathway analyses are powerful tools for generating new insights from statistical enrichment of gene features. Here we present the LnCompare web server, an equivalent resource for studying the properties of lncRNA gene sets. The Gene Set Feature Comparison mode tests for enrichment amongst a panel of quantitative and categorical

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Griffin, Aaron T., Lukas J. Vlahos, Codruta Chiuzan, and Andrea Califano. "NaRnEA: An Information Theoretic Framework for Gene Set Analysis." Entropy 25, no. 3 (2023): 542. http://dx.doi.org/10.3390/e25030542.

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Gene sets are being increasingly leveraged to make high-level biological inferences from transcriptomic data; however, existing gene set analysis methods rely on overly conservative, heuristic approaches for quantifying the statistical significance of gene set enrichment. We created Nonparametric analytical-Rank-based Enrichment Analysis (NaRnEA) to facilitate accurate and robust gene set analysis with an optimal null model derived using the information theoretic Principle of Maximum Entropy. By measuring the differential activity of ~2500 transcriptional regulatory proteins based on the diffe

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Ellis, John, Stephen Goodswen, Paul J. Kennedy, and Stephen Bush. "The Core Mouse Response to Infection by Neospora Caninum Defined by Gene Set Enrichment Analyses." Bioinformatics and Biology Insights 6 (January 2012): BBI.S9954. http://dx.doi.org/10.4137/bbi.s9954.

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In this study, the BALB/c and Qs mouse responses to infection by the parasite Neospora caninum were investigated in order to identify host response mechanisms. Investigation was done using gene set (enrichment) analyses of microarray data. GSEA, MANOVA, Romer, subGSE and SAM-GS were used to study the contrasts Neospora strain type, Mouse type (BALB/c and Qs) and time post infection (6 hours post infection and 10 days post infection). The analyses show that the major signal in the core mouse response to infection is from time post infection and can be defined by gene ontology terms Protein Kina

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Luo, Weijun, Michael S. Friedman, Kerby Shedden, Kurt D. Hankenson, and Peter J. Woolf. "GAGE: generally applicable gene set enrichment for pathway analysis." BMC Bioinformatics 10, no. 1 (2009): 161. http://dx.doi.org/10.1186/1471-2105-10-161.

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Hung, J. H., T. H. Yang, Z. Hu, Z. Weng, and C. DeLisi. "Gene set enrichment analysis: performance evaluation and usage guidelines." Briefings in Bioinformatics 13, no. 3 (2011): 281–91. http://dx.doi.org/10.1093/bib/bbr049.

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KLEBANOV, LEV, GALINA GLAZKO, PETER SALZMAN, ANDREI YAKOVLEV, and YUANHUI XIAO. "A MULTIVARIATE EXTENSION OF THE GENE SET ENRICHMENT ANALYSIS." Journal of Bioinformatics and Computational Biology 05, no. 05 (2007): 1139–53. http://dx.doi.org/10.1142/s0219720007003041.

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A test-statistic typically employed in the gene set enrichment analysis (GSEA) prevents this method from being genuinely multivariate. In particular, this statistic is insensitive to changes in the correlation structure of the gene sets of interest. The present paper considers the utility of an alternative test-statistic in designing the confirmatory component of the GSEA. This statistic is based on a pertinent distance between joint distributions of expression levels of genes included in the set of interest. The null distribution of the proposed test-statistic, known as the multivariate N-sta

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Oron, Assaf P., Zhen Jiang, and Robert Gentleman. "Gene set enrichment analysis using linear models and diagnostics." Bioinformatics 24, no. 22 (2008): 2586–91. http://dx.doi.org/10.1093/bioinformatics/btn465.

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Ji, Rui-Ru, Karl-Heinz Ott, Roumyana Yordanova, and Bruccoleri. "FDR-FET: an optimizing gene set enrichment analysis method." Advances and Applications in Bioinformatics and Chemistry Volume 4 (March 2011): 37–42. http://dx.doi.org/10.2147/aabc.s15840.

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Li, Lie, Xinlei Wang, Guanghua Xiao, and Adi Gazdar. "Integrative gene set enrichment analysis utilizing isoform-specific expression." Genetic Epidemiology 41, no. 6 (2017): 498–510. http://dx.doi.org/10.1002/gepi.22052.

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Fontaine, Jean Fred, and Miguel A. Andrade-Navarro. "Gene Set to Diseases (GS2D): disease enrichment analysis on human gene sets with literature data." Genomics and Computational Biology 2, no. 1 (2016): 33. http://dx.doi.org/10.18547/gcb.2016.vol2.iss1.e33.

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Large sets of candidate genes derived from high-throughput biological experiments can be characterized by functional enrichment analysis. The analysis consists of comparing the functions of one gene set against that of a background gene set. Then, functions related to a significant number of genes in the gene set are expected to be relevant. Web tools offering disease enrichment analysis on gene sets are often based on gene-disease associations from manually curated or experimental data that is accurate but does not cover all diseases discussed in the literature. Using associations automatical

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Wijesooriya, Kaumadi, Sameer A. Jadaan, Kaushalya L. Perera, Tanuveer Kaur, and Mark Ziemann. "Urgent need for consistent standards in functional enrichment analysis." PLOS Computational Biology 18, no. 3 (2022): e1009935. http://dx.doi.org/10.1371/journal.pcbi.1009935.

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Gene set enrichment tests (a.k.a. functional enrichment analysis) are among the most frequently used methods in computational biology. Despite this popularity, there are concerns that these methods are being applied incorrectly and the results of some peer-reviewed publications are unreliable. These problems include the use of inappropriate background gene lists, lack of false discovery rate correction and lack of methodological detail. To ascertain the frequency of these issues in the literature, we performed a screen of 186 open-access research articles describing functional enrichment resul

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Chen, Shang-Yang, Gaurav Gadhvi, and Deborah R. Winter. "MAGNET: A web-based application for gene set enrichment analysis using macrophage data sets." PLOS ONE 18, no. 1 (2023): e0272166. http://dx.doi.org/10.1371/journal.pone.0272166.

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Characterization of gene lists obtained from high-throughput genomic experiments is an essential task to uncover the underlying biological insights. A common strategy is to perform enrichment analyses that utilize standardized biological annotations, such as GO and KEGG pathways, which attempt to encompass all domains of biology. However, this approach provides generalized, static results that may fail to capture subtleties associated with research questions within a specific domain. Thus, there is a need for an application that can provide precise, relevant results by leveraging the latest re

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Liang, Xiangdong, and Bin Liu. "Exploration of PVT1 as a biomarker in prostate cancer." Medicine 103, no. 34 (2024): e39406. http://dx.doi.org/10.1097/md.0000000000039406.

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Prostate cancer is a malignant tumor originating from the prostate gland, significantly affecting patients’ quality of life and survival rates. Public data was utilized to identify differentially expressed genes (DEGs). Weighted gene co-expression network analysis was constructed to classify gene modules. Functional enrichment analysis was performed through Kyoto Encyclopedia of Genes and Genomes and gene ontology annotations, with results visualized using the Metascape database. Additionally, gene set enrichment analysis evaluated gene expression profiles and related pathways, constructed a p

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McGovern, Kyle C., Michelle Pistner Nixon, and Justin D. Silverman. "Addressing erroneous scale assumptions in microbe and gene set enrichment analysis." PLOS Computational Biology 19, no. 11 (2023): e1011659. http://dx.doi.org/10.1371/journal.pcbi.1011659.

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By applying Differential Set Analysis (DSA) to sequence count data, researchers can determine whether groups of microbes or genes are differentially enriched. Yet sequence count data suffer from a scale limitation: these data lack information about the scale (i.e., size) of the biological system under study, leading some authors to call these data compositional (i.e., proportional). In this article, we show that commonly used DSA methods that rely on normalization make strong, implicit assumptions about the unmeasured system scale. We show that even small errors in these scale assumptions can

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Holdorf, Amy D., Daniel P. Higgins, Anne C. Hart, et al. "WormCat: An Online Tool for Annotation and Visualization of Caenorhabditis elegans Genome-Scale Data." Genetics 214, no. 2 (2019): 279–94. http://dx.doi.org/10.1534/genetics.119.302919.

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The emergence of large gene expression datasets has revealed the need for improved tools to identify enriched gene categories and visualize enrichment patterns. While gene ontogeny (GO) provides a valuable tool for gene set enrichment analysis, it has several limitations. First, it is difficult to graph multiple GO analyses for comparison. Second, genes from some model systems are not well represented. For example, ∼30% of Caenorhabditis elegans genes are missing from the analysis in commonly used databases. To allow categorization and visualization of enriched C. elegans gene sets in differen

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Yang, Yarong, Eric J. Kort, Nader Ebrahimi, Zhongfa Zhang, and Bin T. Teh. "Dual KS: Defining Gene Sets with Tissue Set Enrichment Analysis." Cancer Informatics 9 (January 2010): CIN.S2892. http://dx.doi.org/10.4137/cin.s2892.

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Background Gene set enrichment analysis (GSEA) is an analytic approach which simultaneously reduces the dimensionality of microarray data and enables ready inference of the biological meaning of observed gene expression patterns. Here we invert the GSEA process to identify class-specific gene signatures. Because our approach uses the Kolmogorov-Smirnov approach both to define class specific signatures and to classify samples using those signatures, we have termed this methodology “Dual-KS” (DKS). Results The optimum gene signature identified by the DKS algorithm was smaller than other methods

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Wenzel, Alexander T., John Jun, and Jill P. Mesirov. "Abstract 2343: Adapting gene set enrichment analysis to single cell data." Cancer Research 84, no. 6_Supplement (2024): 2343. http://dx.doi.org/10.1158/1538-7445.am2024-2343.

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Abstract Gene Set Enrichment Analysis (GSEA) is a method for quantifying the activation of pathways and processes in gene expression data. GSEA works by ranking genes and testing if genes in annotated gene sets representing molecular pathways are overrepresented at the top or bottom of the ranked list. Standard GSEA assumes a gene-by-sample matrix with samples falling in two phenotype class labels and ranks genes based on correlation of expression and sample class labels. Single sample (ssGSEA) uses a similar approach to enrichment scoring but ranks genes in a single sample based on their expr

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Komljenovic, Andrea, Julien Roux, Marc Robinson-Rechavi, and Frederic B. Bastian. "BgeeDB, an R package for retrieval of curated expression datasets and for gene list expression localization enrichment tests." F1000Research 5 (November 23, 2016): 2748. http://dx.doi.org/10.12688/f1000research.9973.1.

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BgeeDB is a collection of functions to import into R re-annotated, quality-controlled and reprocessed expression data available in the Bgee database. This includes data from thousands of wild-type healthy samples of multiple animal species, generated with different gene expression technologies (RNA-seq, Affymetrix microarrays, expressed sequence tags, and in situ hybridizations). BgeeDB facilitates downstream analyses, such as gene expression analyses with other Bioconductor packages. Moreover, BgeeDB includes a new gene set enrichment test for preferred localization of expression of genes in

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Komljenovic, Andrea, Julien Roux, Julien Wollbrett, Marc Robinson-Rechavi, and Frederic B. Bastian. "BgeeDB, an R package for retrieval of curated expression datasets and for gene list expression localization enrichment tests." F1000Research 5 (August 7, 2018): 2748. http://dx.doi.org/10.12688/f1000research.9973.2.

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BgeeDB is a collection of functions to import into R re-annotated, quality-controlled and re-processed expression data available in the Bgee database. This includes data from thousands of wild-type healthy samples of multiple animal species, generated with different gene expression technologies (RNA-seq, Affymetrix microarrays, expressed sequence tags, and in situ hybridizations). BgeeDB facilitates downstream analyses, such as gene expression analyses with other Bioconductor packages. Moreover, BgeeDB includes a new gene set enrichment test for preferred localization of expression of genes in

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Alhamdoosh, Monther, Charity W. Law, Luyi Tian, Julie M. Sheridan, Milica Ng, and Matthew E. Ritchie. "Easy and efficient ensemble gene set testing with EGSEA." F1000Research 6 (November 14, 2017): 2010. http://dx.doi.org/10.12688/f1000research.12544.1.

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Gene set enrichment analysis is a popular approach for prioritising the biological processes perturbed in genomic datasets. The Bioconductor project hosts over 80 software packages capable of gene set analysis. Most of these packages search for enriched signatures amongst differentially regulated genes to reveal higher level biological themes that may be missed when focusing only on evidence from individual genes. With so many different methods on offer, choosing the best algorithm and visualization approach can be challenging. The EGSEA package solves this problem by combining results from up

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Meng, YuXiu, Xue Hong Cai, and LiPei Wang. "Potential Genes and Pathways of Neonatal Sepsis Based on Functional Gene Set Enrichment Analyses." Computational and Mathematical Methods in Medicine 2018 (July 30, 2018): 1–10. http://dx.doi.org/10.1155/2018/6708520.

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Background. Neonatal sepsis (NS) is considered as the most common cause of neonatal deaths that newborns suffer from. Although numerous studies focus on gene biomarkers of NS, the predictive value of the gene biomarkers is low. NS pathogenesis is still needed to be investigated. Methods. After data preprocessing, we used KEGG enrichment method to identify the differentially expressed pathways between NS and normal controls. Then, functional principal component analysis (FPCA) was adopted to calculate gene values in NS. In order to further study the key signaling pathway of the NS, elastic-net

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Lin, Cally, Jingchun Zhu, and Mary Goldman. "Abstract 6293: Developing and deploying the UCSC Xena gene set enrichment analysis appyter." Cancer Research 85, no. 8_Supplement_1 (2025): 6293. https://doi.org/10.1158/1538-7445.am2025-6293.

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Abstract UCSC Xena (https://xena.ucsc.edu) is a popular web-based visual integration and exploration tool for multiomic data and associated clinical and phenotypic annotations. Xena users want to be able to run Gene Set Enrichment Analysis (GSEA) to compare gene expression between their two dynamically created sample subgroups and determine which gene programs are upregulated or downregulated. However, the traditional GSEA implementation was not performant enough to support Xena’s interactive web-based data visualizations. To overcome this challenge, blitzGSEA, a more performant implementation

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Hua, Jianping, Michael L. Bittner, and Edward R. Dougherty. "Evaluating Gene Set Enrichment Analysis via a Hybrid Data Model." Cancer Informatics 13s1 (January 2014): CIN.S13305. http://dx.doi.org/10.4137/cin.s13305.

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Gene set enrichment analysis (GSA) methods have been widely adopted by biological labs to analyze data and generate hypotheses for validation. Most of the existing comparison studies focus on whether the existing GSA methods can produce accurate P-values; however, practitioners are often more concerned with the correct gene-set ranking generated by the methods. The ranking performance is closely related to two critical goals associated with GSA methods: the ability to reveal biological themes and ensuring reproducibility, especially for small-sample studies. We have conducted a comprehensive s

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Wiebe, Daniil S., Nadezhda A. Omelyanchuk, Aleksei M. Mukhin, et al. "Fold-Change-Specific Enrichment Analysis (FSEA): Quantification of Transcriptional Response Magnitude for Functional Gene Groups." Genes 11, no. 4 (2020): 434. http://dx.doi.org/10.3390/genes11040434.

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Gene expression profiling data contains more information than is routinely extracted with standard approaches. Here we present Fold-Change-Specific Enrichment Analysis (FSEA), a new method for functional annotation of differentially expressed genes from transcriptome data with respect to their fold changes. FSEA identifies Gene Ontology (GO) terms, which are shared by the group of genes with a similar magnitude of response, and assesses these changes. GO terms found by FSEA are fold-change-specifically (e.g., weakly, moderately, or strongly) affected by a stimulus under investigation. We demon

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Yi, Xin, Zhou Du, and Zhen Su. "PlantGSEA: a gene set enrichment analysis toolkit for plant community." Nucleic Acids Research 41, W1 (2013): W98—W103. http://dx.doi.org/10.1093/nar/gkt281.

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Subramanian, Aravind, Heidi Kuehn, Joshua Gould, Pablo Tamayo, and Jill P. Mesirov. "GSEA-P: a desktop application for Gene Set Enrichment Analysis." Bioinformatics 23, no. 23 (2007): 3251–53. http://dx.doi.org/10.1093/bioinformatics/btm369.

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Lu, Wentao, Xinlei Wang, Xiaowei Zhan, and Adi Gazdar. "Meta-analysis approaches to combine multiple gene set enrichment studies." Statistics in Medicine 37, no. 4 (2017): 659–72. http://dx.doi.org/10.1002/sim.7540.

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Deng, Xiaoxu, and Jeffrey Thompson. "An R package for survival-based gene set enrichment analysis." PeerJ 13 (July 11, 2025): e19489. https://doi.org/10.7717/peerj.19489.

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Functional enrichment analysis is usually used to assess the effects of experimental differences. However, researchers sometimes want to understand the relationship between transcriptomic variation and health outcomes like survival. Therefore, we suggest the use of Survival-based Gene Set Enrichment Analysis (SGSEA) to help determine biological functions associated with a disease’s survival. Despite the availability of this method to researchers, there are no standard tools or software to perform this analysis. We developed an R package and Shiny app called SGSEA and presented a study of kidne

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