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An autoregressive model predicts the next element of a sequence from the ones before it; trained on DNA it learns to continue a genome, which is how Evo 2 was built.
An autoregressive model expresses each output as a function of its own previous values (Wikipedia); large language models are trained this way on text to generate the next token (Wikipedia on LLMs). Genomic models such as Evo 2 and the Nucleotide Transformer apply it to nucleotide sequence at up to a million bases of context, and the likelihood the model assigns to a variant sequence is used as a zero-shot pathogenicity score. Generation and scoring are different uses, and success at one does not imply the other.
Showing the technology this term belongs to: Nucleotide Transformer (InstaDeep).
Shares Genomic and protein language models: Evo 2, Enformer, ESM, Transformer and attention, Cancer AI vocabulary (CanSim terms map) and the tag cansim-terms.
Shares Zero-shot prediction, Transformer and attention, Cancer AI vocabulary (CanSim terms map) and the tag cansim-terms.
Shares Zero-shot prediction, Cancer AI vocabulary (CanSim terms map) and the tag cansim-terms.
Shares Transformer and attention, Cancer AI vocabulary (CanSim terms map) and the tag cansim-terms.
Shares Transformer and attention, Cancer AI vocabulary (CanSim terms map) and the tag cansim-terms.
Shares Variant effect prediction, Cancer AI vocabulary (CanSim terms map) and the tag cansim-terms.
Shares Variant effect prediction, Cancer AI vocabulary (CanSim terms map) and the tag cansim-terms.
Shares Transformer and attention, Cancer AI vocabulary (CanSim terms map) and the tag cansim-terms.