Looking for applications of mixtures of Markov trees in bioinformatics
François Schnitzler, Pierre Geurts, Louis A. Wehenkel · ORBi (University of Liège) · 2011
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.