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dc.contributor.author | Fresno, Cristóbal | |
dc.contributor.author | Llera, Andrea S | |
dc.contributor.author | Girotti, María R | |
dc.contributor.author | Valacco, María P | |
dc.contributor.author | Lopez, Juan Antonio | |
dc.contributor.author | Podhajcer, Osvaldo L | |
dc.contributor.author | Balzarini, Mónica G | |
dc.contributor.author | Prada, Federico | |
dc.contributor.author | Fernández, Elmer A | |
dc.date.accessioned | 2019-05-23T14:04:05Z | |
dc.date.available | 2019-05-23T14:04:05Z | |
dc.date.issued | 2012-02 | |
dc.identifier.citation | Comput Biol Med. 2012; 42(2):188-94 | es_ES |
dc.identifier.issn | 00104825 | es_ES |
dc.identifier.uri | http://hdl.handle.net/20.500.12105/7672 | |
dc.description.abstract | Set enrichment analysis (SEA) is used to identify enriched biological categories/terms within high-throughput differential expression experiments. This is done by evaluating the proportion of differentially expressed genes against a background reference (BR). However, the choice of the "appropriate" BR is a perplexing problem and results will depend on it. Here, a visualization procedure that integrates results from several BRs and a stability analysis of enriched terms is presented as a tool to aid SEA. The multi-reference contrast method (MRCM) combines results from multiple BRs in a unique picture. The application of the proposed method was illustrated in one proteomic and three microarray experiments. The MRCM facilitates the exploration task involved in ontology analysis on proteomic/genomic experiments, where consensus terms were found to validate main experimental hypothesis. The use of more than one reference may provide new biological insights. The tool automatically highlights non-consensus terms assisting SEA. | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | Elsevier | es_ES |
dc.type.hasVersion | AM | es_ES |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject.mesh | Animals | es_ES |
dc.subject.mesh | Cluster Analysis | es_ES |
dc.subject.mesh | Data Mining | es_ES |
dc.subject.mesh | Electrophoresis, Gel, Two-Dimensional | es_ES |
dc.subject.mesh | Gene Expression Profiling | es_ES |
dc.subject.mesh | Genomics | es_ES |
dc.subject.mesh | Humans | es_ES |
dc.subject.mesh | Mice | es_ES |
dc.subject.mesh | Models, Theoretical | es_ES |
dc.subject.mesh | Oligonucleotide Array Sequence Analysis | es_ES |
dc.subject.mesh | Proteomics | es_ES |
dc.subject.mesh | Databases, Genetic | es_ES |
dc.subject.mesh | Terminology as Topic | es_ES |
dc.title | The multi-reference contrast method: facilitating set enrichment analysis | es_ES |
dc.type | journal article | es_ES |
dc.rights.license | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.identifier.pubmedID | 22226646 | es_ES |
dc.format.volume | 42 | es_ES |
dc.format.number | 2 | es_ES |
dc.format.page | 188-94 | es_ES |
dc.identifier.doi | 10.1016/j.compbiomed.2011.11.007 | es_ES |
dc.description.peerreviewed | Sí | es_ES |
dc.identifier.e-issn | 1879-0534 | es_ES |
dc.relation.publisherversion | https://doi.org/10.1016/j.compbiomed.2011.11.007 | es_ES |
dc.identifier.journal | Computers in biology and medicine | es_ES |
dc.repisalud.orgCNIC | CNIC::Unidades técnicas::Proteómica / Metabolómica | es_ES |
dc.repisalud.institucion | CNIC | es_ES |
dc.rights.accessRights | open access | es_ES |