Copy-number alterations for tumor progression inference (Articolo in rivista)

Type
Label
  • Copy-number alterations for tumor progression inference (Articolo in rivista) (literal)
Anno
  • 2013-01-01T00:00:00+01:00 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi
  • 10.1007/978-3-642-38326-7_16 (literal)
Alternative label
  • Cava C.1, Zoppis I.2, Gariboldi M.3,4, Castiglioni I.1, Mauri G.2, Antoniotti M.2 (2013)
    Copy-number alterations for tumor progression inference
    in Lecture notes in computer science
    (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
  • Cava C.1, Zoppis I.2, Gariboldi M.3,4, Castiglioni I.1, Mauri G.2, Antoniotti M.2 (literal)
Pagina inizio
  • 104 (literal)
Pagina fine
  • 109 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#url
  • http://www.scopus.com/inward/record.url?eid=2-s2.0-84887293821&partnerID=q2rCbXpz (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroVolume
  • 7885 LNAI (literal)
Rivista
Note
  • Scopu (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
  • 1IBFM-CNR, 20090 Segrate (MI), Italy. 2Department of Informatics, Systems and Communications, University of Milano-Bicocca, Milano, Italy. 3Department of Experimental Oncology, Fondazione IRCCS Istituto Nazionale dei Tumori, Milano, Italy. 4Ifom, Fondazione Istituto FIRC Oncologia Molecolare, Milano, Italy. (literal)
Titolo
  • Copy-number alterations for tumor progression inference (literal)
Abstract
  • Copy-number alterations (CNAs) represent an important component of genetic variations and play a significant role in many human diseases. Such alterations are related to certain types of cancers, including those of the pancreas, colon, and breast, among others. CNAs have been used as biomarkers for cancer prognosis in multiple studies, but few works report on the relation of CNAs with the disease progression. In this paper, we provide cases where the inference on the disease progression improves when exploiting CNA information. To this aim, a specific dissimilarity-based representation of patients is given. The employed framework outperforms a typical approach where patients are represented through a set of available attribute values. Three datasets were employed to validate the results of our analysis. © 2013 Springer-Verlag. (literal)
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