On the statistical assessment of classifiers using DNA microarray data (Articolo in rivista)

Type
Label
  • On the statistical assessment of classifiers using DNA microarray data (Articolo in rivista) (literal)
Anno
  • 2006-01-01T00:00:00+01:00 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi
  • 10.1186/1471-2105-7-387 (literal)
Alternative label
  • Ancona, N.; Maglietta, R.; Piepoli, A.; D'Addabbo, A.; Cotugno, R.; Savino, M.; Liuni, S.; Carella, M.; Pesole, G.; Perri, F. (2006)
    On the statistical assessment of classifiers using DNA microarray data
    in BMC bioinformatics
    (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
  • Ancona, N.; Maglietta, R.; Piepoli, A.; D'Addabbo, A.; Cotugno, R.; Savino, M.; Liuni, S.; Carella, M.; Pesole, G.; Perri, F. (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroVolume
  • 7 (literal)
Rivista
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroFascicolo
  • 387 (literal)
Note
  • ISI Web of Science (WOS) (literal)
  • SCImago (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
  • lstituto di Studi sui Sistemi Intelligenti per I'Automazione Unità Operativa di Gastroenterologia, IRCCS, Servizio di Genetica Medica, IRCCS, \"Casa Sollievo della Sofferenza\" Dipartimento di Biochimica e Biologia Molecolare - Universitá di Bari, lstituto di Tecnologie Biomediche - Sede di Bari (literal)
Titolo
  • On the statistical assessment of classifiers using DNA microarray data (literal)
Abstract
  • Background: In this paper we present a method for the statistical assessment of cancer predictors which make use of gene expression profiles. The methodology is applied to a new data set of microarray gene expression data collected in Casa Sollievo della Sofferenza Hospital, Foggia - Italy. The data set is made up of normal (22) and tumor (25) specimens extracted from 25 patients affected by colon cancer. We propose to give answers to some questions which are relevant for the automatic diagnosis of cancer such as: Is the size of the available data set sufficient to build accurate classifiers? What is the statistical significance of the associated error rates? In what ways can accuracy be considered dependant on the adopted classification scheme? How many genes are correlated with the pathology and how many are sufficient for an accurate colon cancer classification? The method we propose answers these questions whilst avoiding the potential pitfalls hidden in the analysis and interpretation of microarray data. (literal)
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