ArticleMolecular biology and evolution2024
The Tree Reconstruction Game: Phylogenetic Reconstruction Using Reinforcement Learning.
Article in Molecular biology and evolution, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- A deep learning-based score to evaluate multiple sequence alignments.Molecular biology and evolution · 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
- Optimizing genomic sampling for demographic and epidemiological inference with Markov decision processes.Genetics · 2026Article
- 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
- Phyloformer: Fast, Accurate, and Versatile Phylogenetic Reconstruction with Deep Neural Networks.Molecular biology and evolution · 2025Article
- Opportunities and Challenges in Applying AI to Evolutionary Morphology.Integrative organismal biology (Oxford, England) · 2024Review
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Authors and funding
6 authors.
Funding
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Abstract
The computational search for the maximum-likelihood phylogenetic tree is an NP-hard problem. As such, current tree search algorithms might result in a tree that is the local optima, not the global one. Here, we introduce a paradigm shift for predicting the maximum-likelihood tree, by approximating long-term gains of likelihood rather than maximizing likelihood gain at each step of the search. Our proposed approach harnesses the power of reinforcement learning to learn an optimal search strategy, aiming at the global optimum of the search space. We show that when analyzing empirical data containing dozens of sequences, the log-likelihood improvement from the starting tree obtained by the reinforcement learning-based agent was 0.969 or higher compared to that achieved by current state-of-the-art techniques. Notably, this performance is attained without the need to perform costly likelihood optimizations apart from the training process, thus potentially allowing for an exponential increase in runtime. We exemplify this for data sets containing 15 sequences of length 18,000 bp and demonstrate that the reinforcement learning-based method is roughly three times faster than the state-of-the-art software. This study illustrates the potential of reinforcement learning in addressing the challenges of phylogenetic tree reconstruction.
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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.