ArticleSystematic biology2020
Accurate Inference of Tree Topologies from Multiple Sequence Alignments Using Deep Learning.
Article in Systematic biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 49 papers.
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Who cites it
49 citing papers in PubMed.
- Machine learning for evolutionary genetics and molecular evolution.Trends in genetics : TIG · 2026Review
- Parameter Estimation from Phylogenetic Trees Using Neural Networks and Ensemble Learning.Systematic biology · 2026Article
- Unifying phylogenetic traversal and deep learning to guide tree exploration.bioRxiv : the preprint server for biology · 2026Article
- Learning to explore tree neighbourhoods for phylogenetic inference.Briefings in bioinformatics · 2026Article
- Efficient algorithms for simulating sequences along a phylogenetic tree.Bioinformatics (Oxford, England) · 2026Article
- A Systematic Review: Deep Learning for Analyzing Genomic Data to Discover Evolutionary Patterns.Scientifica · 2026Review
- phyddle: Software for Exploring Phylogenetic Models with Deep Learning.Systematic biology · 2025Article
- Phylogenetic Methods Meet Deep Learning.Genome biology and evolution · 2025Article
- PhyloTune: An efficient method to accelerate phylogenetic updates using a pretrained DNA language model.Nature communications · 2025Article
- Detecting Interspecific Positive Selection Using Convolutional Neural Networks.Molecular biology and evolution · 2025Article
- Phyloformer: Fast, Accurate, and Versatile Phylogenetic Reconstruction with Deep Neural Networks.Molecular biology and evolution · 2025Article
- Current state and future prospects of Horizontal Gene Transfer detection.NAR genomics and bioinformatics · 2025Review
- phyddle: software for exploring phylogenetic models with deep learning.bioRxiv : the preprint server for biology · 2025Article
- A quartet-based approach for inferring phylogenetically informative features from genomic and phenomic data.Computational and structural biotechnology journal · 2025Article
- Toward a Semi-Supervised Learning Approach to Phylogenetic Estimation.Systematic biology · 2024Article
- Insertions and Deletions: Computational Methods, Evolutionary Dynamics, and Biological Applications.Molecular biology and evolution · 2024Review
- Reliable estimation of tree branch lengths using deep neural networks.PLoS computational biology · 2024Article
- Discovering Fragile Clades and Causal Sequences in Phylogenomics by Evolutionary Sparse Learning.Molecular biology and evolution · 2024Article
- A machine-learning-based alternative to phylogenetic bootstrap.Bioinformatics (Oxford, England) · 2024Article
- Genetic structure and landscape effects on gene flow in the Neotropical lizard Norops brasiliensis (Squamata: Dactyloidae).Heredity · 2024Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Reconstructing the phylogenetic relationships between species is one of the most formidable tasks in evolutionary biology. Multiple methods exist to reconstruct phylogenetic trees, each with their own strengths and weaknesses. Both simulation and empirical studies have identified several "zones" of parameter space where accuracy of some methods can plummet, even for four-taxon trees. Further, some methods can have undesirable statistical properties such as statistical inconsistency and/or the tendency to be positively misleading (i.e. assert strong support for the incorrect tree topology). Recently, deep learning techniques have made inroads on a number of both new and longstanding problems in biological research. In this study, we designed a deep convolutional neural network (CNN) to infer quartet topologies from multiple sequence alignments. This CNN can readily be trained to make inferences using both gapped and ungapped data. We show that our approach is highly accurate on simulated data, often outperforming traditional methods, and is remarkably robust to bias-inducing regions of parameter space such as the Felsenstein zone and the Farris zone. We also demonstrate that the confidence scores produced by our CNN can more accurately assess support for the chosen topology than bootstrap and posterior probability scores from traditional methods. Although numerous practical challenges remain, these findings suggest that the deep learning approaches such as ours have the potential to produce more accurate phylogenetic inferences.
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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.