ChromBPNet and GENA-LM: GUANinE v1.1 reveals complementarity of supervised and genomic language models and Comparative Analysis of Deep Learning Models for Predicting Causative Regulatory Variants disagree
- Low severity
- Not yet reviewed
- two-study-conflict
- unknown
GUANinE v1.1 reveals complementarity of supervised and genomic language models ranks GENA-LM above ChromBPNet in 3 of 4 comparisons; Comparative Analysis of Deep Learning Models for Predicting Causative Regulatory Variants ranks ChromBPNet above GENA-LM in 4 of 4 comparisons. No other study gives a verdict, so neither is the outlier.
An automatic lead, not a finding of error. The results below are shown as published, everywhere on the site, until a reviewer decides otherwise.
Which studies agree
2 papers rank ChromBPNet above GENA-LM in 5 of their 10 comparisons and GENA-LM higher in 3; 2 are ties. Of those papers, GUANinE v1.1 reveals complementarity… ranks GENA-LM higher on its own (3 of 6).
- Rank ChromBPNet higher (1): Comparative Analysis of Deep Learning…
- Rank GENA-LM higher (1): GUANinE v1.1 reveals complementarity…
- Mixed (0): none
Each paper sits where at least two thirds of its comparisons point; Mixed means neither model reaches two thirds. The larger, outlined dot is the paper that disagrees.
| Paper | Role | Ranks ChromBPNet higher | Ranks GENA-LM higher | Ties |
|---|---|---|---|---|
| GUANinE v1.1 reveals complementarity of supervised and genomic language models · DOI | Agrees | 1 | 3 | 2 |
| Comparative Analysis of Deep Learning Models for Predicting Causative Regulatory Variants · DOI | Agrees | 4 | 0 | 0 |
Comparisons in GUANinE v1.1 reveals complementarity… (6)
| Protocol | Dataset | Metric | ChromBPNet | GENA-LM | Higher |
|---|---|---|---|---|---|
| GUANinE v1.1 ccre-propensity, Spearman rho x100 (Table 2) | GUANinE ccre-propensity test set | Spearman (GUANinE v1.1 test set), unitless | 40.4 | 34.9 | ChromBPNet |
| GUANinE v1.1 cons100, Spearman rho x100 (Table 2) | GUANinE cons100 test set | Spearman (GUANinE v1.1 test set), unitless | 41.5 | 56 | GENA-LM |
| GUANinE v1.1 cons30, Spearman rho x100 (Table 2) | GUANinE cons30 test set | Spearman (GUANinE v1.1 test set), unitless | 47.8 | 66.7 | GENA-LM |
| GUANinE v1.1 dnase-propensity, Spearman rho x100 (Table 2) | GUANinE dnase-propensity test set | Spearman (GUANinE v1.1 test set), unitless | 41.5 | 42.8 | GENA-LM |
| GUANinE v1.1 gpra-c, Spearman rho x100 (Table 2) | GUANinE gpra-c test set | Spearman (GUANinE v1.1 test set), unitless | 71.1 | 71.1 | Tie |
| GUANinE v1.1 gpra-d, Spearman rho x100 (Table 2) | GUANinE gpra-d test set | Spearman (GUANinE v1.1 test set), unitless | 74.9 | 74.8 | Tie |
Comparisons in Comparative Analysis of Deep Learning… (4)
| Protocol | Dataset | Metric | ChromBPNet | GENA-LM | Higher |
|---|---|---|---|---|---|
| Allelic reporter effect correlation in HeLa (Manzo et al. 2025 Table 1) | HeLa regulatory variant reporter data (Manzo et al. 2025) | Pearson (predicted versus measured log2 fold-change of variant effect), unitless | 0.289 | 0.026 | ChromBPNet |
| Allelic reporter effect correlation in HepG2 (Manzo et al. 2025 Table 1) | HepG2 regulatory variant reporter data (Manzo et al. 2025) | Pearson (predicted versus measured log2 fold-change of variant effect), unitless | 0.295 | 0.062 | ChromBPNet |
| Allelic reporter effect correlation in K562 (Manzo et al. 2025 Table 1) | K562 regulatory variant reporter data (Manzo et al. 2025) | Pearson (predicted versus measured log2 fold-change of variant effect), unitless | 0.287 | 0.077 | ChromBPNet |
| Allelic reporter effect correlation in NPC (Manzo et al. 2025 Table 1) | NPC regulatory variant reporter data (Manzo et al. 2025) | Pearson (predicted versus measured log2 fold-change of variant effect), unitless | 0.08 | 0.01 | ChromBPNet |
What disagrees
- studies
- 2
- Compared
- ChromBPNet, GENA-LM
- Study that disagrees
- Other studies
- Rule
- two-study-conflict; unknown
- Model identity
- Matched across studies partly through links not yet reviewed
Follow-up
Current state: Not yet reviewed.
No one has reviewed this finding yet.
How to help resolve this
If you know why these studies disagree, for example a different population, metric or preprocessing step, or you have a corrected value, tell us through the contribution form. A reviewer checks every submission against its source before anything changes, and the step is added to the follow-up above.
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