Bayesian methods have found many successful applications in high-throughput genomics. We focus on approaches for network-based inference from gene expression data. Methods that employ sparse priors have been particularly successful, as they are properly designed to analyze large datasets in which the amount of measured variables can be greater than the number of observations. Here, we describe Bayesian approaches for both undirected and directed networks; we dis- cuss novel approaches that are computationally efficient, do not rely on linearity assumptions and perform comparatively better than state-of-the-art methods. We demonstrate the utility of our methods via applications to glioblastoma gene expression data.
Bayesian approaches for large biological networks / Ni, Yang; Marchetti, Giovanni M.; Baladandayuthapani, Veerabhadran; Stingo, Francesco C.. - STAMPA. - (2015), pp. 1-21.
Bayesian approaches for large biological networks
MARCHETTI, GIOVANNI MARIA;STINGO, FRANCESCO CLAUDIO
2015
Abstract
Bayesian methods have found many successful applications in high-throughput genomics. We focus on approaches for network-based inference from gene expression data. Methods that employ sparse priors have been particularly successful, as they are properly designed to analyze large datasets in which the amount of measured variables can be greater than the number of observations. Here, we describe Bayesian approaches for both undirected and directed networks; we dis- cuss novel approaches that are computationally efficient, do not rely on linearity assumptions and perform comparatively better than state-of-the-art methods. We demonstrate the utility of our methods via applications to glioblastoma gene expression data.File | Dimensione | Formato | |
---|---|---|---|
bookChapter2015_bioBNPfinal.pdf
Accesso chiuso
Descrizione: Articolo principale
Tipologia:
Pdf editoriale (Version of record)
Licenza:
Tutti i diritti riservati
Dimensione
677.16 kB
Formato
Adobe PDF
|
677.16 kB | Adobe PDF | Richiedi una copia |
I documenti in FLORE sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.