GenART: An adaptive genomic language model that dynamically discovers biologically meaningful DNA words
Chen et al. introduce GenART, an adaptive genomic language model that dynamically segments raw DNA sequences into biologically meaningful "words" of variable length, rather than using fixed-length k-mers or single nucleotides. This approach overcomes the arbitrary nature of traditional DNA tokenization, improving predictive performance across diverse genomic tasks while autonomously capturing functional genomic boundaries. The model enhances interpretability by learning context-dependent representations that reflect biological function, addressing a key limitation in genomic language modeling where DNA lacks natural delimiters like spaces or punctuation found in human language.
Key quotes
DNA lacks natural delimiters, and current models usually segment sequences using single nucleotides, fixed-length k-mers, or statistically derived subwords, which do not explicitly reflect biological context or function.
These tokenization strategies limit interpretability and can compromise generalization across diverse genomic tasks.
GenART, an adaptive genomic language model that dynamically discovers variable-length... biologically meaningful 'words.'
This adaptive approach overcomes arbitrary sequence splitting, boosting predictive performance across diverse tasks while autonomously capturing functional genomic boundaries to enhance interpretability.
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