ArticleNucleic acids research2023
Fusang: a framework for phylogenetic tree inference via deep learning.
Article in Nucleic acids research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Fast and accurate identification of emerging viral reassortment from genome sequences.Nucleic acids research · 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
- A quartet-based approach for inferring phylogenetically informative features from genomic and phenomic data.Computational and structural biotechnology journal · 2025Article
- Insertions and Deletions: Computational Methods, Evolutionary Dynamics, and Biological Applications.Molecular biology and evolution · 2024Review
- 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
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Authors and funding
10 authors.
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Abstract
Phylogenetic tree inference is a classic fundamental task in evolutionary biology that entails inferring the evolutionary relationship of targets based on multiple sequence alignment (MSA). Maximum likelihood (ML) and Bayesian inference (BI) methods have dominated phylogenetic tree inference for many years, but BI is too slow to handle a large number of sequences. Recently, deep learning (DL) has been successfully applied to quartet phylogenetic tree inference and tentatively extended into more sequences with the quartet puzzling algorithm. However, no DL-based tools are immediately available for practical real-world applications. In this paper, we propose Fusang (http://fusang.cibr.ac.cn), a DL-based framework that achieves comparable performance to that of ML-based tools with both simulated and real datasets. More importantly, with continuous optimization, e.g. through the use of customized training datasets for real-world scenarios, Fusang has great potential to outperform ML-based tools.
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