Non-bifurcating phylogenetic tree inference via the adaptive LASSO
arXiv:1805.11073
Abstract
Phylogenetic tree inference using deep DNA sequencing is reshaping our understanding of rapidly evolving systems, such as the within-host battle between viruses and the immune system. Densely sampled phylogenetic trees can contain special features, including "sampled ancestors" in which we sequence a genotype along with its direct descendants, and "polytomies" in which multiple descendants arise simultaneously. These features are apparent after identifying zero-length branches in the tree. However, current maximum-likelihood based approaches are not capable of revealing such zero-length branches. In this paper, we find these zero-length branches by introducing adaptive-LASSO-type regularization estimators to phylogenetics, deriving their properties, and showing regularization to be a practically useful approach for phylogenetics.
References in corpus (18)
- Rejoinder to "Least angle regression" by Efron et al
- Discussion of "Least angle regression" by Efron et al
- Discussion of "Least angle regression" by Efron et al
- Least Angle Regression
- Discussion of "Least angle regression" by Efron et al
- Discussion of "Least angle regression" by Efron et al
- Discussion of "Least angle regression" by Efron et al
- Discussion of "Least angle regression" by Efron et al
- Discussion of "Least angle regression" by Efron et al
- Discussion of "Least angle regression" by Efron et al
- Simultaneous analysis of Lasso and Dantzig selector
- High-dimensional regression with noisy and missing data: Provable guarantees with nonconvexity
- Bayesian total evidence dating reveals the recent crown radiation of penguins
- Strong oracle optimality of folded concave penalized estimation
- Regularized M-estimators with nonconvexity: Statistical and algorithmic theory for local optima
- Discussion: One-step sparse estimates in nonconcave penalized likelihood models
- Probabilistic Path Hamiltonian Monte Carlo
- Fast learning rates with heavy-tailed losses