Metric learning for phylogenetic invariants
| dc.creator | Eriksson, Nicholas | |
| dc.creator | Yao, Yuan | |
| dc.date | 2007-03-15 | |
| dc.date.accessioned | 2026-07-07T07:52:09Z | |
| dc.date.available | 2026-07-07T07:52:09Z | |
| dc.description | We introduce new methods for phylogenetic tree quartet construction by using machine learning to optimize the power of phylogenetic invariants. Phylogenetic invariants are polynomials in the joint probabilities which vanish under a model of evolution on a phylogenetic tree. We give algorithms for selecting a good set of invariants and for learning a metric on this set of invariants which optimally distinguishes the different models. Our learning algorithms involve linear and semidefinite programming on data simulated over a wide range of parameters. We provide extensive tests of the learned metrics on simulated data from phylogenetic trees with four leaves under the Jukes-Cantor and Kimura 3-parameter models of DNA evolution. Our method greatly improves on other uses of invariants and is competitive with or better than neighbor-joining. In particular, we obtain metrics trained on trees with short internal branches which perform much better than neighbor joining on this region of parameter space. | |
| dc.description | 12 pages, 6 figures | |
| dc.identifier | https://arxiv.org/abs/q-bio/0703034 | |
| dc.identifier | http://arxiv.org/abs/q-bio/0703034 | |
| dc.identifier.uri | http://salesiana.dossiersoluciones.com/handle/123456789/125778 | |
| dc.subject | Populations and Evolution | |
| dc.title | Metric learning for phylogenetic invariants | |
| dc.type | text |