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| Format: | Preprint |
| Veröffentlicht: |
2025
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| Online-Zugang: | https://arxiv.org/abs/2512.02223 |
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| _version_ | 1866914177277231104 |
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| author | Rosenzweig, Benjamin K. Hahn, Matthew W. |
| author_facet | Rosenzweig, Benjamin K. Hahn, Matthew W. |
| contents | Inferring the phylogenetic relationships among a sample of organisms is a fundamental problem in modern biology. While distance-based hierarchical clustering algorithms achieved early success on this task, these have been supplanted by Bayesian and maximum likelihood search procedures based on complex models of molecular evolution. In this work we describe minimal neural network architectures that can approximate classic phylogenetic distance functions and the properties required to learn distances under a variety of molecular evolutionary models. In contrast to model-based inference (and recently proposed model-free convolutional and transformer networks), these architectures have a small computational footprint and are scalable to large numbers of taxa and molecular characters. The learned distance functions generalize well and, given an appropriate training dataset, achieve results comparable to state-of-the art inference methods. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_02223 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | On the Approximation of Phylogenetic Distance Functions by Artificial Neural Networks Rosenzweig, Benjamin K. Hahn, Matthew W. Machine Learning Populations and Evolution Inferring the phylogenetic relationships among a sample of organisms is a fundamental problem in modern biology. While distance-based hierarchical clustering algorithms achieved early success on this task, these have been supplanted by Bayesian and maximum likelihood search procedures based on complex models of molecular evolution. In this work we describe minimal neural network architectures that can approximate classic phylogenetic distance functions and the properties required to learn distances under a variety of molecular evolutionary models. In contrast to model-based inference (and recently proposed model-free convolutional and transformer networks), these architectures have a small computational footprint and are scalable to large numbers of taxa and molecular characters. The learned distance functions generalize well and, given an appropriate training dataset, achieve results comparable to state-of-the art inference methods. |
| title | On the Approximation of Phylogenetic Distance Functions by Artificial Neural Networks |
| topic | Machine Learning Populations and Evolution |
| url | https://arxiv.org/abs/2512.02223 |