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NT (2B51000G), Self-supervised pre-training (Tang et al. 2025)

NT (2B51000G), Self-supervised pre-training (Tang et al. 2025) as evaluated in the cited comparison.

Overview

NT (2B51000G), Self-supervised pre-training (Tang et al. 2025) as evaluated in the cited comparison.

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Evaluations and results

2 evaluations · 2 results. Different protocols are not a single leaderboard.

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Tested configuration	Protocol and dataset	Finding	Evidence and details
Configuration: NT (2B51000G), Self-supervised pre-training (Tang et al. 2025)	Protocol: CAGI5 saturation MPRA variant effect correlation, HepG2 (Tang et al. 2025 Table 1) Dataset: CAGI5 saturation mutagenesis MPRA, four CREs (as used by Tang et al. 2025)	0.125 pearson-correlation unitless · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Methods, coverage and source NT (2B51000G) (Self-supervised pre-training) on CAGI5 HepG2 regulatory-variant-20261009-protocol-tang2025-cagi5-hepg2 Aggregation: Not reported Evaluating the representational power of pre-trained DNA language models for regulatory genomics · Table 1 row 'Self-supervised pre-training' / 'NT (2B51000G)', column 'HepG2'
Configuration: NT (2B51000G), Self-supervised pre-training (Tang et al. 2025)	Protocol: CAGI5 saturation MPRA variant effect correlation, K562 (Tang et al. 2025 Table 1) Dataset: CAGI5 saturation mutagenesis MPRA, four CREs (as used by Tang et al. 2025)	0.007 pearson-correlation unitless · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Methods, coverage and source NT (2B51000G) (Self-supervised pre-training) on CAGI5 K562 regulatory-variant-20261009-protocol-tang2025-cagi5-k562 Aggregation: Not reported Evaluating the representational power of pre-trained DNA language models for regulatory genomics · Table 1 row 'Self-supervised pre-training' / 'NT (2B51000G)', column 'K562'

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Release 2026-10-10-6e93f504adfc · Record review: source checked

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Evaluating the representational power of pre-trained DNA language models for regulatory genomics · Original source · Genome Biology 26:203, published 2025-07-14; PMC12261763 full-text XML

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Technical metadata and extraction receipts

Stable ID: regulatory-variant-20261009-config-tang2025-self-supervised-pre-training-nt-2b51000g

areas: dna-genomes
contexts: research
method types: foundation_model
reported name: NT (2B51000G), Self-supervised pre-training (Tang et al. 2025)
foundation model eligible: true
parameters: Zero-shot: cosine similarity of embeddings for mutant and wild-type sequence (NT, HyenaDNA) or log2 ratio of masked-nucleotide predictions (GPN); 230 nt sequences centred on each CRE
source locator: Table 1 row 'Self-supervised pre-training' / 'NT (2B51000G)'; Methods 'CAGI dataset'
missing metadata: version: reason: unreported; note: The table prints only the label 'NT (2B51000G)'; no release or checkpoint identifier.

Related records

configuration of: Nucleotide Transformer
system: NT (2B51000G) (Self-supervised pre-training) on CAGI5 HepG2
system: NT (2B51000G) (Self-supervised pre-training) on CAGI5 K562

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