# Distinguishing level-1 phylogenetic networks on the basis of data generated by Markov processes

@article{Gross2020DistinguishingLP, title={Distinguishing level-1 phylogenetic networks on the basis of data generated by Markov processes}, author={Elizabeth Gross and Leo van Iersel and Remie Janssen and Mark Jones and Colby Long and Yukihiro Murakami}, journal={Journal of Mathematical Biology}, year={2020}, volume={83} }

Phylogenetic networks can represent evolutionary events that cannot be described by phylogenetic trees. These networks are able to incorporate reticulate evolutionary events such as hybridization, introgression, and lateral gene transfer. Recently, network-based Markov models of DNA sequence evolution have been introduced along with model-based methods for reconstructing phylogenetic networks. For these methods to be consistent, the network parameter needs to be identifiable from data generated…

## 7 Citations

### Invariants for level-1 phylogenetic networks under the Cavendar-Farris-Neyman Model

- Mathematics
- 2021

This paper determines all quadratic invariants in the sunlet network ideal which is conjecture generate the full ideal of the Cavendar-Farris-Neyman (CFN) model on level-1 phylogenetic networks.

### Distinguishing Level-2 Phylogenetic Networks Using Phylogenetic Invariants

- Mathematics
- 2021

This paper studies the distinguishability of phylogenetic network models associated with level-2 networks using an algebraic approach, namely using discrete Fourier transformation, which generalize earlier work on the distinguishable of level-1 networks.

### Statistical learning with phylogenetic network invariants

- Biology
- 2022

This work proposes a method of utilizing invariant residuals and support vector machines to infer 4-leaf level-one phylogenetic networks, from which larger networks can be reconstructed.

### Ultrafast learning of 4-node hybridization cycles in phylogenetic networks using algebraic invariants

- Biology
- 2022

This work is the first to define phylogenetic invariants on concordance factors (frequencies of 4-taxon splits in the input gene trees) to identify level-1 phylogenetic networks under the multispecies coalescent model.

### Classes of explicit phylogenetic networks and their biological and mathematical significance

- BiologyJournal of Mathematical Biology
- 2022

A thorough review of several subclasses of rooted phylogenetic networks (characterized by certain structural constraints) introduced in the literature, either to model specific biological phenomena or to enable tractable mathematical and computational analyses.

### Identifiability of species network topologies from genomic sequences using the logDet distance

- Biology, Computer ScienceJournal of Mathematical Biology
- 2022

It is shown that logDet distances computed from genomic-scale sequences retain sufficient information to recover network relationships in the level-1 ultrametric case, and under standard stochastic models statistically justifiable inference of network relationships from sequences can be accomplished without consideration of individual genes or gene trees.

### Identifiability of local and global features of phylogenetic networks from average distances

- Computer Science
- 2021

The “distance split tree” is proposed, which can be constructed from pairwise distances, and it is proved that it is a refinement of the network’s tree of blobs, capturing the tree-like features of the networks, including polytomies from blobs.

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