Bayesian models for the two-sample time-course microarray experiments (Articolo in rivista)

Type

• Articolo in rivista (Classe)
• Prodotto della ricerca (Classe)

Label

• Bayesian models for the two-sample time-course microarray experiments (Articolo in rivista) (literal)

Anno

• 2009-01-01T00:00:00+01:00 (literal)

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

• 10.1016/j.csda.2008.07.015 (literal)

Alternative label

• Angelini C.; De Canditiis D.; Pensky M. (2009)
  Bayesian models for the two-sample time-course microarray experiments
  in Computational statistics & data analysis (Print)
  (literal)

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

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

Pagina inizio

• 1547 (literal)

Pagina fine

• 1565 (literal)

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

• http://www.sciencedirect.com/science/article/pii/S0167947308003496 (literal)

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

• 53 (literal)

Rivista

• Computational statistics & data analysis (Rivista)

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

• 5 (literal)

Note

• Scopu (literal)
• Mathematical Reviews on the web (MathSciNet) (literal)
• ISI Web of Science (WOS) (literal)
• Google S (literal)

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

• Istituto per le Applicazioni del Calcolo, `Mauro Picone', CNR, Italy Department of Mathematics, University of Central Florida, USA (literal)

Titolo

• Bayesian models for the two-sample time-course microarray experiments (literal)

Abstract

• A truly functional Bayesian method for detecting temporally differentially expressed genes between two experimental conditions is presented. The method distinguishes between two biologically different set ups, one in which the two samples are interchangeable, and one in which the second sample is a modification of the first, i.e. the two samples are non-interchangeable. This distinction leads to two different Bayesian models, which allow more flexibility in modeling gene expression profiles. The method allows one to identify differentially expressed genes, to rank them and to estimate their expression profiles. The proposed procedure successfully deals with various technical difficulties which arise in microarray time-course experiments, such as small number of observations, non-uniform sampling intervals and presence of missing data or repeated measurements. The procedure allows one to account for various types of error, thus offering a good compromise between nonparametric and normality assumption based techniques. In addition, all evaluations are carried out using analytic expressions, hence the entire procedure requires very little computational effort. The performance of the procedure is studied using simulated and real data. (literal)

Prodotto di

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

Autore CNR

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

Insieme di parole chiave

• Parole chiave di "Bayesian models for the two-sample time-course microarray experiments" (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)

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

• Computational statistics & data analysis (Rivista)

Insieme di parole chiave di

• Parole chiave di "Bayesian models for the two-sample time-course microarray experiments" (Insieme di parole chiave)