Advancing Divide-And-Conquer Phylogeny Estimation Using Robinson-Foulds Supertrees

One of the Grand Challenges in Science is the construction of the Tree of Life, an evolutionary tree containing several million species, spanning all life on earth. However, the construction of the Tree of Life is enormously computationally challenging, as all the current most accurate methods are either heuristics for NP-hard optimization problems or Bayesian MCMC methods that sample from tree space. One of the most promising approaches for improving scalability and accuracy for phylogeny estimation uses divide-and-conquer: a set of species is divided into overlapping subsets, trees are constructed on the subsets, and then merged together using a "supertree method". Here, we present Exact-RFS-2, the first polynomial-time algorithm to find an optimal supertree of two trees, using the Robinson-Foulds Supertree (RFS) criterion (a major approach in supertree estimation that is related to maximum likelihood supertrees), and we prove that finding the RFS of three input trees is NP-hard. We also present GreedyRFS (a greedy heuristic that operates by repeatedly using Exact-RFS-2 on pairs of trees, until all the trees are merged into a single supertree). We evaluate Exact-RFS-2 and GreedyRFS, and show that they have better accuracy than the current leading heuristic for RFS

Local Quartet Splits of a Binary Tree Infer All Quartet Splits Via One Dyadic Inference Rule

A significant problem in phylogeny is to reconstruct a semilabelled binary tree from few valid quartet splits of it. It is well-known that every semilabelled binary tree is determined by its set of all valid quartet splits. Here we strengthen this result by showing that its local (i.e. small diameter) quartet splits infer by a dyadic inference rule all valid quartet splits, and hence determine the tree.  The results of the paper also present a polynomial time algorithm to recover the tree

Local Quartet Splits of a Binary Tree Infer All Quartet Splits Via One Dyadic Inference Rule

A significant problem in phylogeny is to reconstruct a semilabelled binary tree from few valid quartet splits of it. It is well-known that every semilabelled binary tree is determined by its set of all valid quartet splits. Here we strengthen this result by showing that its local (i.e. small diameter) quartet splits infer by a dyadic inference rule all valid quartet splits, and hence determine the tree. The results of the paper also present a polynomial time algorithm to recover the tree. Keywords.semilabelled binary trees, subtrees, phylogeny, quartets. 1 Acknowledgment. This research started when the authors enjoyed the hospitality of DIMACS during the Special Year for Mathematical Support to Molecular Biology. The second author gratefully acknowledges the New Zealand Ministry of Research, Science and Technology (MORST) for support to visit Budapest under ISAC Programme grant 94/22. Research of the first and third authors was supported in part by the Hungarian National Science Fund..

Gaining Insight into Determinants of Physical Activity using Bayesian Network Learning

Contains fulltext : 228326pre.pdf (preprint version ) (Open Access) Contains fulltext : 228326pub.pdf (publisher's version ) (Open Access)BNAIC/BeneLearn 202

A comparison of the CAR and DAGAR spatial random effects models with an application to diabetics rate estimation in Belgium

When hierarchically modelling an epidemiological phenomenon on a finite collection of sites in space, one must always take a latent spatial effect into account in order to capture the correlation structure that links the phenomenon to the territory. In this work, we compare two autoregressive spatial models that can be used for this purpose: the classical CAR model and the more recent DAGAR model. Differently from the former, the latter has a desirable property: its ρ parameter can be naturally interpreted as the average neighbor pair correlation and, in addition, this parameter can be directly estimated when the effect is modelled using a DAGAR rather than a CAR structure. As an application, we model the diabetics rate in Belgium in 2014 and show the adequacy of these models in predicting the response variable when no covariates are available

A Statistical Approach to the Alignment of fMRI Data

Multi-subject functional Magnetic Resonance Image studies are critical. The anatomical and functional structure varies across subjects, so the image alignment is necessary. We define a probabilistic model to describe functional alignment. Imposing a prior distribution, as the matrix Fisher Von Mises distribution, of the orthogonal transformation parameter, the anatomical information is embedded in the estimation of the parameters, i.e., penalizing the combination of spatially distant voxels. Real applications show an improvement in the classification and interpretability of the results compared to various functional alignment methods