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Looking for applications of mixtures of Markov trees in bioinformatics
Schnitzler, François; Geurts, Pierre; Wehenkel, Louis
2011
 

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Keywords :
bayesian networks; mixture of trees; Markov trees
Abstract :
[en] Probabilistic graphical models (PGM) efficiently encode a probability distribution on a large set of variables. While they have already had several successful applications in biology, their poor scaling in terms of the number of variables may make them unfit to tackle problems of increasing size. Mixtures of trees however scale well by design. Experiments on synthetic data have shown the interest of our new learning methods for this model, and we now wish to apply them to relevant problems in bioinformatics.
Research center :
Systèmes et Modélisation
Disciplines :
Computer science
Author, co-author :
Schnitzler, François ;  Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Systèmes et modélisation
Geurts, Pierre ;  Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Systèmes et modélisation
Wehenkel, Louis  ;  Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Systèmes et modélisation
Language :
English
Title :
Looking for applications of mixtures of Markov trees in bioinformatics
Publication date :
21 March 2011
Number of pages :
A0
Event name :
BioMAGNet Annual Meeting 2011
Event place :
Bruxelles, Belgium
Funders :
FRIA - Fonds pour la Formation à la Recherche dans l'Industrie et dans l'Agriculture [BE]
Biomagnet IUAP network of the Belgian Science Policy Office
Pascal2 network of excellence of the EC
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since 24 March 2011

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