ArticleNature communications2021
Harnessing machine learning to guide phylogenetic-tree search algorithms.
Article in Nature communications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
37 citing papers in PubMed.
- Phylogenomic subsampling and upsampling for efficient evolutionary analyses of big data.Molecular biology and evolution · 2026Article
- A deep learning-based score to evaluate multiple sequence alignments.Molecular biology and evolution · 2026Article
- Establishing a combined rational design protocol for the discovery of novel peptide binders of FGF7.Molecular diversity · 2026Article
- Phylogenomic subsampling and upsampling for efficient evolutionary analyses of big data.bioRxiv : the preprint server for biology · 2026Article
- Full-Body AI Agent: A Perspective on Multi-Scale Collaborative AI for Systemic Biology and Precision Medicine.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- 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
- Review
- Accurate and Efficient Phylogenetic Inference through End-To-End Deep Learning.Molecular biology and evolution · 2025Article
- A Multi-Layered Framework for Modeling Human Biology: From Basic AI Agents to a Full-Body AI Agent.ArXiv · 2025Article
- Discovering novel type I collagen fragments from Cyprinus carpio supporting bone regeneration.Functional & integrative genomics · 2025Article
- Phyloformer: Fast, Accurate, and Versatile Phylogenetic Reconstruction with Deep Neural Networks.Molecular biology and evolution · 2025Article
- Poplar: a phylogenomics pipeline.Bioinformatics advances · 2025Article
- Cancer phylogenetic inference using copy number alterations detected from DNA sequencing data.Cancer pathogenesis and therapy · 2025Review
- Insertions and Deletions: Computational Methods, Evolutionary Dynamics, and Biological Applications.Molecular biology and evolution · 2024Review
- Modeling Substitution Rate Evolution across Lineages and Relaxing the Molecular Clock.Genome biology and evolution · 2024Review
- Reliable estimation of tree branch lengths using deep neural networks.PLoS computational biology · 2024Article
- A machine-learning-based alternative to phylogenetic bootstrap.Bioinformatics (Oxford, England) · 2024Article
- The Tree Reconstruction Game: Phylogenetic Reconstruction Using Reinforcement Learning.Molecular biology and evolution · 2024Article
- Common Methods for Phylogenetic Tree Construction and Their Implementation in R.Bioengineering (Basel, Switzerland) · 2024Review
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Inferring a phylogenetic tree is a fundamental challenge in evolutionary studies. Current paradigms for phylogenetic tree reconstruction rely on performing costly likelihood optimizations. With the aim of making tree inference feasible for problems involving more than a handful of sequences, inference under the maximum-likelihood paradigm integrates heuristic approaches to evaluate only a subset of all potential trees. Consequently, existing methods suffer from the known tradeoff between accuracy and running time. In this proof-of-concept study, we train a machine-learning algorithm over an extensive cohort of empirical data to predict the neighboring trees that increase the likelihood, without actually computing their likelihood. This provides means to safely discard a large set of the search space, thus potentially accelerating heuristic tree searches without losing accuracy. Our analyses suggest that machine learning can guide tree-search methodologies towards the most promising candidate trees.
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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.