University of Tasmania
Browse

Multiple Maxima of Likelihood in Phylogenetic Trees: An Analytic Approach

Version 2 2024-09-17, 02:01
Version 1 2023-05-23, 04:52
conference contribution
posted on 2024-09-17, 02:01 authored by B Chor, MD Hendy, Barbara HollandBarbara Holland, D Penny
Maximum likelihood (ML) is a widely used criterion for selecting optimal evolutionary trees. However, little is known on the nature of the likelihood surface for trees, especially as to the frequency of multiple optima. We initiate an analytic study for identifying sequences that generate multiple optima. We report a new approach to calculating ML directly, which we have used to find large families of sequences that have multiple optima, including sequences with a continuum of optimal points. Such datasets are best supported by different (two or more) phylogenies that vary significantly in their timings of evolutionary events. Some standard biological processes can lead to data with multiple optima and consequently the field needs further investigation. Our results imply that hill climbing techniques, as currently implemented in various software packages, cannot guarantee to find the global ML point, even if it is unique.

History

Publication title

RECOMB 2000: Proceedings of the Fourth Annual International Conference on Computational Molecular Biology

Volume

4

Editors

R Shamir, S Mijano, S Istrail, P Pevzner and M Waterman

Pagination

108-117

ISBN

1-58113-186-0

Department/School

School of Natural Sciences

Publisher

The Association for Computing Machinery

Publication status

  • Published

Place of publication

New York, US

Event title

RECOMB: Fourth Annual International Conference on Computational Molecular Biology

Event Venue

Tokyo, Japan

Date of Event (Start Date)

2000-04-08

Date of Event (End Date)

2000-04-11

Socio-economic Objectives

280118 Expanding knowledge in the mathematical sciences

Usage metrics

    University Of Tasmania

    Exports

    RefWorks
    BibTeX
    Ref. manager
    Endnote
    DataCite
    NLM
    DC