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Sparse Gaussian graphical models for dynamic gene regulatory networks

Presented by: 
Veronica Vinciotti Brunel University
Date: 
Wednesday 14th December 2016 - 11:15 to 12:00
Venue: 
INI Seminar Room 1
Abstract: 

 

Co-authors: Luigi Augugliaro (University of Palermo), Antonino Abbruzzo (University of Palermo), Ernst Wit (University of Groningen)  
In this talk, I will present a factorial Gaussian graphical model for inferring dynamic gene regulatory networks from genomic high-throughput data. The model allows including dynamic-related equality constraints on the precision matrix as well as imposing sparsity constraints in the estimation procedure. I will discuss model selection and present an application on a high-resolution time-course microarray data from the Neisseria meningitidis bacterium, a causative agent of life-threatening infections such as meningitis. The methodology described in this paper is implemented in the R package sglasso, freely available from CRAN.

 

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Presentation Material: 
University of Cambridge Research Councils UK
    Clay Mathematics Institute London Mathematical Society NM Rothschild and Sons