Global functional profiling of gene expression.

By Sorin Draghici, Purvesh Khatri, R. P. Martins, G. Charles Ostermeier, Stephen A. Krawetz. Genomics, 2003

487 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/0c2753df73bb3671bad8ef48763443d8f7a213ef

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Citations in the corpus

GOTree Machine (GOTM): a web-based platform for interpreting sets of interesting genes using Gene Ontology hierarchies (2004, methodology): “Statistical tests that have been used for the assessment of enrichment by related published software include Fisher's exact test, χ2 test, T test and binomial test [8-12].”
Enrichment Map: A Network-Based Method for Gene-Set Enrichment Visualization and Interpretation (2010, background): “Enrichment analysis assesses the over-(or under-) representation of a known set of genes (e.g. a biological pathway) within the input gene list [6,7,8].”
Context-Specific Metabolic Networks Are Consistent with Experiments (2008, methodology): “Pathway-based methods, for example [10], attempt to avoid the noise problem by assuming that all mRNAs assigned to a particular pathway should be present or absent together.”
Biomedical Ontologies in Action: Role in Knowledge Management, Data Integration and Decision Support (2008, background): “The simultaneous activity (or inactivity) of genes in these groups represents only one clue into their participation in biological activities and such groups of genes generally require further characterization, especially through functional annotations [140].”
The Reactome Pathway Knowledgebase (2017, methodology): “The Reactome implementation uses a hypergeometric distribution test to generate a probability score, which is corrected for false discovery rate using the Benjamani-Hochberg method (9,10).”
Systematic survey reveals general applicability of "guilt-by-association" within gene coexpression networks (2005, background): “By exploiting the insight that subtle but coordinated changes in expression can be detected by combining measurements across multiple members of a functional module, these focused studies have successfully found specific modules that are important in diabetes [12], aging [13], and cancer [10,11,14,1”
GENECODIS: a web-based tool for finding significant concurrent annotations in gene lists (2007, methodology): “For a detailed description of these methods in the context of the ontological analysis of gene lists, see the work of Draghici and coworkers [9] and the online help for the program.”
MetPA: a web-based metabolomics tool for pathway analysis and visualization (2010, methodology): “Additional features include a comprehensive compound library for metabolite name conversion, automatic generation of analysis report, as well as the implementation of various univariate statistical procedures that can be accessed when users click on any metabolite node on a pathway map.”
GENOMIC DATA ANALYSIS (2004, methodology): “For the case of clearly defined subsets of genes, many authors have employed Fisher's exact test or variants thereof (Zeeberg et al., 2003; Draghici et al., 2003; Beissbarth and Speed, 2004).”