Constructing Unrooted Phylogenetic Trees with Reinforcement Learning

Lipták, Panna; Kiss, Attila

doi:10.24193/subbi.2021.1.03

Cited by 2 publications

(1 citation statement)

References 18 publications

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“…Two recent studies employed RL to phylogeny ( Liptak and Kiss 2021 ; Zhu and Cai 2021 ). In these studies, RL was used in the context of distance-based methods, which are known to be faster, albeit less accurate than likelihood-based methods ( Saitou and Nei 1987 ; Ogden and Rosenberg 2006 ).…”

Section: Discussionmentioning

confidence: 99%

The Tree Reconstruction Game: Phylogenetic Reconstruction Using Reinforcement Learning

Azouri,

Granit,

Alburquerque

et al. 2024

Molecular Biology and Evolution

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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 datasets containing 15 sequences of length 18,000 base-pairs, 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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Section: Discussionmentioning

confidence: 99%

The Tree Reconstruction Game: Phylogenetic Reconstruction Using Reinforcement Learning

Azouri,

Granit,

Alburquerque

et al. 2024

Molecular Biology and Evolution

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Evaluating Feature Impact Prior to Phylogenetic Analysis Using Machine Learning Techniques

Salman,

Hosszú

2024

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The purpose of this paper is to describe a feature selection algorithm and its application to enhance the accuracy of the reconstruction of phylogenetic trees by improving the efficiency of tree construction. Applying machine learning models for Arabic and Aramaic scripts, such as deep neural networks (DNNs), support vector machines (SVMs), and random forests (RFs), each model was used to compare the phylogenies. The methodology was applied to a dataset containing Arabic and Aramaic scripts, demonstrating its relevance in a range of phylogenetic analyses. The results emphasize that feature selection by DNNs, their essential role, outperforms other models in terms of area under the curve (AUC) and equal error rate (EER) across various datasets and fold sizes. Furthermore, both SVM and RF models are valuable for understanding the strengths and limitations of these approaches in the context of phylogenetic analysis This method not only simplifies the tree structures but also enhances their Consistency Index values. Therefore, they offer a robust framework for evolutionary studies. The findings highlight the application of machine learning in phylogenetics, suggesting a path toward accurate and efficient evolutionary analyses and enabling a deeper understanding of evolutionary relationships.

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Constructing Unrooted Phylogenetic Trees with Reinforcement Learning

Cited by 2 publications

References 18 publications

The Tree Reconstruction Game: Phylogenetic Reconstruction Using Reinforcement Learning

The Tree Reconstruction Game: Phylogenetic Reconstruction Using Reinforcement Learning

Evaluating Feature Impact Prior to Phylogenetic Analysis Using Machine Learning Techniques

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