ArticleMolecular biology and evolution2020
Distinguishing Felsenstein Zone from Farris Zone Using Neural Networks.
Article in Molecular biology and evolution, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
13 citing papers in PubMed.
- A deep learning-based score to evaluate multiple sequence alignments.Molecular biology and evolution · 2026Article
- IQ-TREE 3: phylogenomic inference software using complex evolutionary models.Molecular biology and evolution · 2026Article
- Efficient algorithms for simulating sequences along a phylogenetic tree.Bioinformatics (Oxford, England) · 2026Article
- Predicting Phylogenetic Bootstrap Values via Machine Learning.Molecular biology and evolution · 2024Article
- Reliable estimation of tree branch lengths using deep neural networks.PLoS computational biology · 2024Article
- Novel symmetry-preserving neural network model for phylogenetic inference.Bioinformatics advances · 2024Article
- Article
- AliSim-HPC: parallel sequence simulator for phylogenetics.Bioinformatics (Oxford, England) · 2023Article
- Compositionally Constrained Sites Drive Long-Branch Attraction.Systematic biology · 2023Article
- AliSim: A Fast and Versatile Phylogenetic Sequence Simulator for the Genomic Era.Molecular biology and evolution · 2022Article
- phastSim: Efficient simulation of sequence evolution for pandemic-scale datasets.PLoS computational biology · 2022Article
- phastSim: efficient simulation of sequence evolution for pandemic-scale datasets.bioRxiv : the preprint server for biology · 2021Article
- Harnessing machine learning to guide phylogenetic-tree search algorithms.Nature communications · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
Maximum likelihood and maximum parsimony are two key methods for phylogenetic tree reconstruction. Under certain conditions, each of these two methods can perform more or less efficiently, resulting in unresolved or disputed phylogenies. We show that a neural network can distinguish between four-taxon alignments that were evolved under conditions susceptible to either long-branch attraction or long-branch repulsion. When likelihood and parsimony methods are discordant, the neural network can provide insight as to which tree reconstruction method is best suited to the alignment. When applied to the contentious case of Strepsiptera evolution, our method shows robust support for the current scientific view, that is, it places Strepsiptera with beetles, distant from flies.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.