A Method to Classify Microarray Data (Contributo in volume (capitolo o saggio))

Type
Label
  • A Method to Classify Microarray Data (Contributo in volume (capitolo o saggio)) (literal)
Anno
  • 2003-01-01T00:00:00+01:00 (literal)
Alternative label
  • Scarnò M., Sforzini D., Ulivieri A. and Nasi S. (2003)
    A Method to Classify Microarray Data
    Springer, Berlin Heidelberg (Germania) in Between Data Science and Applied Data Analysis. Springer Berlin Heidelberg, 2003. 680-688., 2003
    (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
  • Scarnò M., Sforzini D., Ulivieri A. and Nasi S. (literal)
Pagina inizio
  • 680 (literal)
Pagina fine
  • 688 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#altreInformazioni
  • Scarno, M., Sforzini, D., Ulivieri, A., & Nasi, S. (2003). A Method to Classify Microarray Data. In Between Data Science and Applied Data Analysis (pp. 680-688). Springer Berlin Heidelberg. (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#citta
  • Basel-Boston-Berlin (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#titoloVolume
  • Between Data Science and Applied Data Analysis. Springer Berlin Heidelberg, 2003. 680-688. (literal)
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  • 9 (literal)
Note
  • Google Scholar (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
  • Scarnò M, Sforzini D: CASPUR, Università di Roma La Sapienza Ulivieri A, Nasi S:Istituto di Biologia e Patologia Molecolari CNR (literal)
Titolo
  • A Method to Classify Microarray Data (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#inCollana
  • Between Data Science and Applied Data Analysis (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#isbn
  • 354040354X (literal)
Abstract
  • DNA microarrays are a technology that is used for monitoring the expression of thousand genes simultaneously. Microarray data create new opportunities for biomedical sciences but also pose serious challenges for statistical analysis, due to their complexity. Inference of biological meaning from gene expression profiling involves the classification of genes whose expression is correlated to a given functional condition. To test the value of Correspondence Analysis for microarray data classification, we compared gene expression profiles of cells in the presence or absence of a dominant negative Myc mutant. As compared to other microarray analysis methods, this procedure allows the projection of a complex data set into an easily visualized, low dimensional space, in which association between variables (genes) and observations (experiments) can be more easily evaluated. Correspondence Analysis is a powerful technique for gene-expression data analysis, particularly when combined with other classification techniques. (literal)
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