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This review surveys the emergence and development of genome language models (gLMs) in bioinformatics, highlighting their ability to model complex genomic sequences as biological texts. It discusses various gLM architectures, including Transformer models and Hyena convolutions, as well as tokenization strategies and pretraining methodologies, while addressing challenges such as data scarcity and interpretability. The authors propose a roadmap for future advancements in gLMs, emphasizing their transformative

Author

Tahir Ali

Language

EN