Looking for applications of mixtures of Markov trees in bioinformatics

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.