Abstracts
- 09/01/2012 - V. A. Huynh-Thu
- 09/01/2012 - R. Küffner
- 09/01/2012 - C. Ambroise
- 16/09/2011 - M. Bunster
- 27/04/2011 - P. Meyer
- 06/04/2011 - D. Débois
- 30/03/2011 - J. Wouters
- 28/01/2011 - J.-C. Lambert
- 13/12/2010 - J. Giard
- 17/11/2010 - B. Charloteaux
- 12/05/2010 - P. Soumillion
- 30/04/2010 - J.-P. Vert
- 14/04/2010 - X. Tordoir
- 24/03/2010 - J.C. Voegel
- 17/03/2010 - A. Turtoi
- 24/02/2010 - L. Palmeira
- 27/01/2010 - D. Devos
- 09/12/2009 - F. Heuze
- 25/11/2009 - L. Geris
- 19/11/2009 - V. Gabelica
- 14/10/2009 - G. Zocchi
- 30/09/2009 - K. Van Steen
- 12/06/2009 - D. Gianola
- 05/06/2009 - F. J. Theis
- 02/06/2009 - M. Babu
- 07/05/2009 - J. Eyzaguirre
- 22/04/2009 - E. Bullinger
- 08/04/2009 - F. Francis
- 04/03/2009 - Kick-off
Network Inference based on Mutual Information Applied to Microarray Data
Patrick Meyer
Date and place: Wednesday April, 27th 12:30 pm at R7 (B28 - Montefiore)
An important issue in computational biology is the extent to which it is possible to learn transcriptional interactions from measured expression data. The reverse engineering of transcriptional regulatory networks from expression data alone is challenging because of the combinatorial nature of the problem and of the limited amount of (noisy) samples available in expression datasets.
This talk will focus on information-theoretic approaches of network inference which typically rely on the estimation of mutual information and/or conditional mutual information from data in order to measure the statistical dependence between genes expressions. The adoption of mutual information in network inference can be traced back to Chow and Liu's tree algorithm. Nowadays, two main categories of information-theoretic network inference methods hold the attention of the bioinformatics community: i) methods based on bivariate mutual information that infer undirected networks up to thousands of genes thanks to their low algorithmic complexity and ii) methods based on conditional mutual information that are able to infer a larger set of relationships between genes but at the price of a higher algorithmic complexity. The strengths and weaknesses of these information-theoretic methods for inferring transcriptional networks will be detailed in this talk.