Sparse Gaussian graphical models for dynamic gene regulatory networks
Duration: 41 mins 54 secs
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Description: |
Vinciotti, V (Brunel University)
Wednesday 14th December 2016 - 11:15 to 12:00 |
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Created: | 2016-12-20 12:31 |
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Collection: | Theoretical Foundations for Statistical Network Analysis |
Publisher: | Isaac Newton Institute |
Copyright: | Vinciotti, V |
Language: | eng (English) |
Distribution: | World (downloadable) |
Explicit content: | No |
Aspect Ratio: | 16:9 |
Screencast: | No |
Bumper: | UCS Default |
Trailer: | UCS Default |
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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