A Phylogenetic Approach to Genomic Language Modeling
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866918398627151872 |
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| author | Albors, Carlos Li, Jianan Canal Benegas, Gonzalo Ye, Chengzhong Song, Yun S. |
| author_facet | Albors, Carlos Li, Jianan Canal Benegas, Gonzalo Ye, Chengzhong Song, Yun S. |
| contents | Genomic language models (gLMs) have shown mostly modest success in identifying evolutionarily constrained elements in mammalian genomes. To address this issue, we introduce a novel framework for training gLMs that explicitly models nucleotide evolution on phylogenetic trees using multispecies whole-genome alignments. Our approach integrates an alignment into the loss function during training but does not require it for making predictions, thereby enhancing the model's applicability. We applied this framework to train PhyloGPN, a model that excels at predicting functionally disruptive variants from a single sequence alone and demonstrates strong transfer learning capabilities. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_03773 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Phylogenetic Approach to Genomic Language Modeling Albors, Carlos Li, Jianan Canal Benegas, Gonzalo Ye, Chengzhong Song, Yun S. Genomics Machine Learning Genomic language models (gLMs) have shown mostly modest success in identifying evolutionarily constrained elements in mammalian genomes. To address this issue, we introduce a novel framework for training gLMs that explicitly models nucleotide evolution on phylogenetic trees using multispecies whole-genome alignments. Our approach integrates an alignment into the loss function during training but does not require it for making predictions, thereby enhancing the model's applicability. We applied this framework to train PhyloGPN, a model that excels at predicting functionally disruptive variants from a single sequence alone and demonstrates strong transfer learning capabilities. |
| title | A Phylogenetic Approach to Genomic Language Modeling |
| topic | Genomics Machine Learning |
| url | https://arxiv.org/abs/2503.03773 |