| Configuration: CNN-GPN, LentiMPRA-embedding (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) | 0.332 pearson-correlation unitless · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible | Author-reported evaluation · Source checkedMethods, coverage and sourceCNN-GPN (LentiMPRA-embedding) 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 'LentiMPRA-embedding' / 'CNN-GPN', column 'HepG2' |
|---|
| Configuration: CNN-NT, LentiMPRA-embedding (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) | 0.185 pearson-correlation unitless · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible | Author-reported evaluation · Source checkedMethods, coverage and sourceCNN-NT (LentiMPRA-embedding) 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 'LentiMPRA-embedding' / 'CNN-NT', column 'HepG2' |
|---|
| Configuration: CNN-SEI, LentiMPRA-embedding (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) | 0.579 pearson-correlation unitless · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible | Author-reported evaluation · Source checkedMethods, coverage and sourceCNN-SEI (LentiMPRA-embedding) 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 'LentiMPRA-embedding' / 'CNN-SEI', column 'HepG2' |
|---|
| Configuration: CNN, LentiMPRA-one-hot (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) | 0.324 pearson-correlation unitless · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible | Author-reported evaluation · Source checkedMethods, coverage and sourceCNN (LentiMPRA-one-hot) 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 'LentiMPRA-one-hot' / 'CNN', column 'HepG2' |
|---|
| Configuration: MPRAnn, LentiMPRA-one-hot (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) | 0.381 pearson-correlation unitless · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourceMPRAnn (LentiMPRA-one-hot) 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 'LentiMPRA-one-hot' / 'MPRAnn', column 'HepG2' |
|---|
| Configuration: Residualbind, LentiMPRA-one-hot (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) | 0.485 pearson-correlation unitless · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible | Author-reported evaluation · Source checkedMethods, coverage and sourceResidualbind (LentiMPRA-one-hot) 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 'LentiMPRA-one-hot' / 'Residualbind', column 'HepG2' |
|---|
| Configuration: GPN (human), 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) | 0.002 pearson-correlation unitless · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourceGPN (human) (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' / 'GPN (human)', column 'HepG2' |
|---|
| Configuration: HyenaDNA, 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) | 0.064 pearson-correlation unitless · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourceHyenaDNA (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' / 'HyenaDNA', column 'HepG2' |
|---|
| 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 checkedMethods, coverage and sourceNT (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 (2B5Species), 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) | 0.112 pearson-correlation unitless · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourceNT (2B5Species) (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 (2B5Species)', column 'HepG2' |
|---|
| Configuration: NT (500M1000G), 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) | 0.041 pearson-correlation unitless · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourceNT (500M1000G) (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 (500M1000G)', column 'HepG2' |
|---|
| Configuration: NT (500MHuman), 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) | 0.02 pearson-correlation unitless · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourceNT (500MHuman) (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 (500MHuman)', column 'HepG2' |
|---|
| Configuration: Enformer (DNase), Supervised one-hot (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) | 0.51 pearson-correlation unitless · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourceEnformer (DNase) (Supervised one-hot) 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 'Supervised one-hot' / 'Enformer (DNase)', column 'HepG2' |
|---|
| Configuration: SEI, Supervised one-hot (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) | 0.545 pearson-correlation unitless · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourceSEI (Supervised one-hot) 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 'Supervised one-hot' / 'SEI', column 'HepG2' |
|---|
| Configuration: CNN-GPN, LentiMPRA-embedding (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) | 0.437 pearson-correlation unitless · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible | Author-reported evaluation · Source checkedMethods, coverage and sourceCNN-GPN (LentiMPRA-embedding) 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 'LentiMPRA-embedding' / 'CNN-GPN', column 'K562' |
|---|
| Configuration: CNN-NT, LentiMPRA-embedding (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) | 0.198 pearson-correlation unitless · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible | Author-reported evaluation · Source checkedMethods, coverage and sourceCNN-NT (LentiMPRA-embedding) 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 'LentiMPRA-embedding' / 'CNN-NT', column 'K562' |
|---|
| Configuration: CNN-SEI, LentiMPRA-embedding (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) | 0.701 pearson-correlation unitless · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible | Author-reported evaluation · Source checkedMethods, coverage and sourceCNN-SEI (LentiMPRA-embedding) 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 'LentiMPRA-embedding' / 'CNN-SEI', column 'K562' |
|---|
| Configuration: CNN, LentiMPRA-one-hot (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) | 0.365 pearson-correlation unitless · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible | Author-reported evaluation · Source checkedMethods, coverage and sourceCNN (LentiMPRA-one-hot) 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 'LentiMPRA-one-hot' / 'CNN', column 'K562' |
|---|
| Configuration: MPRAnn, LentiMPRA-one-hot (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) | 0.437 pearson-correlation unitless · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourceMPRAnn (LentiMPRA-one-hot) 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 'LentiMPRA-one-hot' / 'MPRAnn', column 'K562' |
|---|
| Configuration: Residualbind, LentiMPRA-one-hot (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) | 0.601 pearson-correlation unitless · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible | Author-reported evaluation · Source checkedMethods, coverage and sourceResidualbind (LentiMPRA-one-hot) 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 'LentiMPRA-one-hot' / 'Residualbind', column 'K562' |
|---|
| Configuration: GPN (human), 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) | 0.037 pearson-correlation unitless · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourceGPN (human) (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' / 'GPN (human)', column 'K562' |
|---|
| Configuration: HyenaDNA, 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) | 0.021 pearson-correlation unitless · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourceHyenaDNA (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' / 'HyenaDNA', column 'K562' |
|---|
| 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 checkedMethods, coverage and sourceNT (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' |
|---|
| Configuration: NT (2B5Species), 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) | 0.135 pearson-correlation unitless · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourceNT (2B5Species) (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 (2B5Species)', column 'K562' |
|---|
| Configuration: NT (500M1000G), 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) | 0.068 pearson-correlation unitless · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourceNT (500M1000G) (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 (500M1000G)', column 'K562' |
|---|