ArticleMolecular biology and evolution2020
Deep Residual Neural Networks Resolve Quartet Molecular Phylogenies.
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 33 papers.
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Who cites it
33 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
- Accurate and Efficient Phylogenetic Inference through End-To-End Deep Learning.Molecular biology and evolution · 2025Article
- Phylogenetic Methods Meet Deep Learning.Genome biology and evolution · 2025Article
- Language models reveal a complex sequence basis for adaptive convergent evolution of protein functions.Proceedings of the National Academy of Sciences of the United States of America · 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
- 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
- Exploring geometry of genome space via Grassmann manifolds.Innovation (Cambridge (Mass.)) · 2024Article
- Reliable estimation of tree branch lengths using deep neural networks.PLoS computational biology · 2024Article
- The Tree Reconstruction Game: Phylogenetic Reconstruction Using Reinforcement Learning.Molecular biology and evolution · 2024Article
- Is Over-parameterization a Problem for Profile Mixture Models?Systematic biology · 2024Article
- Common Methods for Phylogenetic Tree Construction and Their Implementation in R.Bioengineering (Basel, Switzerland) · 2024Review
- Novel symmetry-preserving neural network model for phylogenetic inference.Bioinformatics advances · 2024Article
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4 authors.
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Abstract
Phylogenetic inference is of fundamental importance to evolutionary as well as other fields of biology, and molecular sequences have emerged as the primary data for this task. Although many phylogenetic methods have been developed to explicitly take into account substitution models of sequence evolution, such methods could fail due to model misspecification or insufficiency, especially in the face of heterogeneities in substitution processes across sites and among lineages. In this study, we propose to infer topologies of four-taxon trees using deep residual neural networks, a machine learning approach needing no explicit modeling of the subject system and having a record of success in solving complex nonlinear inference problems. We train residual networks on simulated protein sequence data with extensive amino acid substitution heterogeneities. We show that the well-trained residual network predictors can outperform existing state-of-the-art inference methods such as the maximum likelihood method on diverse simulated test data, especially under extensive substitution heterogeneities. Reassuringly, residual network predictors generally agree with existing methods in the trees inferred from real phylogenetic data with known or widely believed topologies. Furthermore, when combined with the quartet puzzling algorithm, residual network predictors can be used to reconstruct trees with more than four taxa. We conclude that deep learning represents a powerful new approach to phylogenetic reconstruction, especially when sequences evolve via heterogeneous substitution processes. We present our best trained predictor in a freely available program named Phylogenetics by Deep Learning (PhyDL, https://gitlab.com/ztzou/phydl; last accessed January 3, 2020).
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