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Activity Number: 514 - Advanced Statistical Inference for Stochastic Models of Evolutionary Biology
Type: Topic Contributed
Date/Time: Wednesday, August 1, 2018 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistics in Genomics and Genetics
Abstract #328516 Presentation
Title: Inferring Non-Bifurcating Phylogenies with the Adaptive Lasso
Author(s): Vu Dinh*
Companies: University Of Delaware
Keywords: phylogenetics; adaptive LASSO; sparsity; l1 regularization; FISTA; model selection
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 sampled 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 work, we introduce adaptive-lasso-type regularization estimators to find these zero-length branches, deriving their properties, and showing regularization to be a practically useful approach for phylogenetics


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