Sparse Gaussian graphical models for dynamic gene regulatory networks
41 mins 54 secs,
610.30 MB,
MPEG-4 Video
640x360,
29.97 fps,
44100 Hz,
1.94 Mbits/sec
Share this media item:
Embed this media item:
Embed this media item:
About this item
| Description: |
Vinciotti, V (Brunel University)
Wednesday 14th December 2016 - 11:15 to 12:00 |
|---|
| Created: | 2016-12-20 12:31 |
|---|---|
| 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. |
|---|---|
Available Formats
| Format | Quality | Bitrate | Size | |||
|---|---|---|---|---|---|---|
| MPEG-4 Video * | 640x360 | 1.94 Mbits/sec | 610.30 MB | View | Download | |
| WebM | 640x360 | 550.03 kbits/sec | 168.87 MB | View | Download | |
| iPod Video | 480x270 | 522.13 kbits/sec | 160.23 MB | View | Download | |
| MP3 | 44100 Hz | 249.81 kbits/sec | 76.73 MB | Listen | Download | |
| Auto | (Allows browser to choose a format it supports) | |||||