Le lundi 06 mai 2013 à 15:00 - UM2 - Bât 09 - Salle de conférence (1er étage)Fabio Pardi
This talk will start with a broad introduction to the field of
phylogenetics and in particular of distance-based methods: several
popular methods for phylogenetic inference (but also hierarchical
clustering) are based on a matrix of estimated distances between taxa
(or any kind of objects); the goal is to construct a tree with edge
lengths so that the distances between the leaves in that tree are as
close as possible to the estimated distances.
The second part of the talk will introduce a novel framework unifying many of the approaches for distance-based inference. In a recent publication , we have shown that all the methods that fit into this general framework have highly desirable statistical properties (the consistency of the tree estimates) and algorithmic properties (efficiency of hill climbing heuristics).
Finally, I will speak about my work on the robustness of distance-based methods: how much can the estimated distances deviate from their 'true' values without compromising the estimation of the 'true' tree topology? It turns out that only one linear optimization principle (among all possible linear principles) guarantees optimal robustness, a very strong result...
 Pardi F, Gascuel O. Combinatorics of distance-based tree inference. Proc Natl Acad Sci USA 109: 16443-16448 (2012).