Bayesian Methods for Time Course Microarray Analysis: From Genes' Detection to Clustering (Contributo in volume (capitolo o saggio))

Type

• Contributo in volume (capitolo o saggio) (Classe)
• Prodotto della ricerca (Classe)

Label

• Bayesian Methods for Time Course Microarray Analysis: From Genes' Detection to Clustering (Contributo in volume (capitolo o saggio)) (literal)

Anno

• 2012-01-01T00:00:00+01:00 (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi

• 10.1007/978-3-642-21037-2 (literal)

Alternative label

• C. Angelini, D. De Canditiis, M. Pensky (2012)
  Bayesian Methods for Time Course Microarray Analysis: From Genes' Detection to Clustering
  Springer, Berlin Heidelberg (Germania) in Advanced Statistical Methods for the Analysis of Large Data-Sets, 2012
  (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori

• C. Angelini, D. De Canditiis, M. Pensky (literal)

Pagina inizio

• 47 (literal)

Pagina fine

• 56 (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#url

• http://link.springer.com/book/10.1007/978-3-642-21037-2/page/1 (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#titoloVolume

• Advanced Statistical Methods for the Analysis of Large Data-Sets (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#volumeInCollana

• Studies in Theoretical and Applied Statistics, SPRINGER (literal)

Note

• ISI Web of Science (WOS) (literal)
• Scopus (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni

• C. Angelini, D. De Canditiis, IAC-CNR, Italy M. Pensky, UCF, USA (literal)

Titolo

• Bayesian Methods for Time Course Microarray Analysis: From Genes' Detection to Clustering (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#isbn

• 978-3-642-21037-2 (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autoriVolume

• Autori vari (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#curatoriVolume

• Di Ciaccio, Agostino; Coli, Mauro; Angulo Ibanez, Jose Miguel (Eds.) (literal)

Abstract

• Time-course microarray experiments are an increasingly popular approach for understanding the dynamical behavior of a wide range of biological systems. In this paper we discuss some recently developed functional Bayesian methods specifically designed for time-course microarray data. The methods allow one to identify differentially expressed genes, to rank them, to estimate their expression profiles and to cluster the genes associated with the treatment according to their behavior across time. The methods successfully deal with various technical difficulties that arise in this type of experiments such as a large number of genes, a small number of observations, non-uniform sampling intervals, missing or multiple data and temporal dependence between observations for each gene. The procedures are illustrated using both simulated and real data. (literal)

Editore

• Springer (Editore)

Prodotto di

• Istituto per le applicazioni del calcolo \"Mauro Picone\" (IAC) (Istituto)
• Algoritmi Statistici orientati al grid computing per la genetica (INT.P02.004.001) (Modulo)

Autore CNR

• DANIELA DE CANDITIIS (Persona)
• CLAUDIA ANGELINI (Unità di personale interno)

Insieme di parole chiave

• Keywords of "Bayesian Methods for Time Course Microarray Analysis: From Genes' Detection to Clustering" (Insieme di parole chiave)

Incoming links:

Prodotto

• Istituto per le applicazioni del calcolo \"Mauro Picone\" (IAC) (Istituto)
• Algoritmi Statistici orientati al grid computing per la genetica (INT.P02.004.001) (Modulo)

Autore CNR di

• CLAUDIA ANGELINI (Unità di personale interno)
• DANIELA DE CANDITIIS (Persona)

Editore di

• Springer (Editore)

Insieme di parole chiave di

• Keywords of "Bayesian Methods for Time Course Microarray Analysis: From Genes' Detection to Clustering" (Insieme di parole chiave)