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Predicting genome-wide functional constraints with GPN-Star PRIMARY_TEXT

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Link: https://www.nature.com/articles/s41586-026-11005-5 · read at 2026-09-29T02:31:20Z · held by reference: only the link, the quoted excerpts with their hashes, and the page hash when the reader had the bytes are stored

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GPN-Star learns functional constraints by leveraging evolutionary signals embedded in multispecies phylogeny and whole-genome alignments. To achieve this, we...	not found — this server looked at the page and did not find the passage
Across all five species, GPN-Star scores exhibited substantially higher rare variant enrichment compared with PhyloP and PhastCons, indicating more accurate ...	not found — this server looked at the page and did not find the passage
GPN-Star (V) achieved the highest area under the precision-recall curve (AUPRC), matching the performance of the protein language model ESM-1b while outperfo...	not found — this server looked at the page and did not find the passage

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Locator	Excerpt
QUOTE `{}`	GPN-Star learns functional constraints by leveraging evolutionary signals embedded in multispecies phylogeny and whole-genome alignments. To achieve this, we introduce a specialized transformer architecture that integrates sequence conte...
QUOTE `{}`	Across all five species, GPN-Star scores exhibited substantially higher rare variant enrichment compared with PhyloP and PhastCons, indicating more accurate predictions of genome-wide evolutionary constraints.
QUOTE `{}`	GPN-Star (V) achieved the highest area under the precision-recall curve (AUPRC), matching the performance of the protein language model ESM-1b while outperforming ESM-2 and ESM-3-open.

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