[2503.03773] A Phylogenetic Approach to Genomic Language Modeling

Quantitative Biology > Genomics arXiv:2503.03773 (q-bio) [Submitted on 4 Mar 2025 (v1), last revised 20 Mar 2026 (this version, v2)] Title:A Phylogenetic Approach to Genomic Language Modeling Authors:Carlos Albors, Jianan Canal Li, Gonzalo Benegas, Chengzhong Ye, Yun S. Song View a PDF of the paper titled A Phylogenetic Approach to Genomic Language Modeling, by Carlos Albors and 4 other authors View PDF HTML (experimental) Abstract: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. Comments: Subjects: Genomics (q-bio.GN); Machine Learning (cs.LG) Cite as: arXiv:2503.03773 [q-bio.GN]   (or arXiv:2503.03773v2 [q-bio.GN] for this version)   https://doi.org/10.48550/arXiv.2503.03773 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Carlos Albors [view email] [v1] Tue, 4 Mar 2025 06:53:03 UTC (1,386 KB) [v2] Fri, 20 Mar 2026 00:45:...