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Title: Novel symmetry-preserving neural network model for phylogenetic inference
Abstract Scientists world-wide are putting together massive efforts to understand how the biodiversity that we see on Earth evolved from single-cell organisms at the origin of life and this diversification process is represented through the Tree of Life. Low sampling rates and high heterogeneity in the rate of evolution across sites and lineages produce a phenomenon denoted “long branch attraction” (LBA) in which long non-sister lineages are estimated to be sisters regardless of their true evolutionary relationship. LBA has been a pervasive problem in phylogenetic inference affecting different types of methodologies from distance-based to likelihood-based. Here, we present a novel neural network model that outperforms standard phylogenetic methods and other neural network implementations under LBA settings. Furthermore, unlike existing neural network models in phylogenetics, our model naturally accounts for the tree isomorphisms via permutation invariant functions which ultimately result in lower memory and allows the seamless extension to larger trees.  more » « less
Award ID(s):
2144367 2012292
PAR ID:
10501229
Author(s) / Creator(s):
; ; ; ;
Editor(s):
Ouangraoua, Aida
Publisher / Repository:
Bioinformatics Advances
Date Published:
Journal Name:
Bioinformatics Advances
ISSN:
2635-0041
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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