ChromBPNet and Enformer: Comparative Analysis of Deep Learning Models for Predicting Causative Regulatory Variants reverses the order
- Low severity
- Not yet reviewed
- Ordering reversal
- Different tasks across studies
Comparative Analysis of Deep Learning Models for Predicting Causative Regulatory Variants ranks ChromBPNet above Enformer in 4 of 4 comparisons; other studies rank Enformer above ChromBPNet in 4 of 5.
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
GUANinE v1.1 reveals complementarity… ranks Enformer above ChromBPNet in 4 of its 7 comparisons and ChromBPNet higher in 1; 2 are ties. Comparative Analysis of Deep Learning… ranks ChromBPNet above Enformer in all 4 of its comparisons.
- Rank Enformer higher (1): GUANinE v1.1 reveals complementarity…
- Rank ChromBPNet higher (1): Comparative Analysis of Deep Learning… (disagrees)
- 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 Enformer higher | Ranks ChromBPNet higher | Ties |
|---|---|---|---|---|
| Comparative Analysis of Deep Learning Models for Predicting Causative Regulatory Variants · DOI | Disagrees | 0 | 4 | 0 |
| GUANinE v1.1 reveals complementarity of supervised and genomic language models · DOI | Agrees | 4 | 1 | 2 |
Comparisons in Comparative Analysis of Deep Learning… (4)
| Protocol | Dataset | Metric | Enformer | ChromBPNet | 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 | 0.245 | 0.289 | 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 | 0.141 | 0.295 | 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 | 0.059 | 0.287 | 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 | 0.008 | 0.08 | ChromBPNet |
Comparisons in GUANinE v1.1 reveals complementarity… (7)
| Protocol | Dataset | Metric | Enformer | ChromBPNet | Higher |
|---|---|---|---|---|---|
| GUANinE v1.1 cadd-snv, Spearman rho x100 (Table 3) | GUANinE cadd-snv test set | Spearman (GUANinE v1.1 test set), unitless | 17.1 | 17.3 | ChromBPNet |
| GUANinE v1.1 ccre-propensity, Spearman rho x100 (Table 2) | GUANinE ccre-propensity test set | Spearman (GUANinE v1.1 test set), unitless | 55.4 | 40.5 | Enformer |
| GUANinE v1.1 cons100, Spearman rho x100 (Table 2) | GUANinE cons100 test set | Spearman (GUANinE v1.1 test set), unitless | 62.4 | 41.5 | Enformer |
| GUANinE v1.1 cons30, Spearman rho x100 (Table 2) | GUANinE cons30 test set | Spearman (GUANinE v1.1 test set), unitless | 74.7 | 47.8 | Enformer |
| GUANinE v1.1 dnase-propensity, Spearman rho x100 (Table 2) | GUANinE dnase-propensity test set | Spearman (GUANinE v1.1 test set), unitless | 68.1 | 41.5 | Enformer |
| GUANinE v1.1 gpra-c, Spearman rho x100 (Table 2) | GUANinE gpra-c test set | Spearman (GUANinE v1.1 test set), unitless | 70.7 | 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 | 74.2 | 74.9 | Tie |
What disagrees
- Other studies
- rank Enformer above ChromBPNet in 4 of 5 comparisons
- This study
- ranks ChromBPNet above Enformer in 4 of 4 comparisons
- Compared
- ChromBPNet, Enformer
- Study that disagrees
- Comparative Analysis of Deep Learning Models for Predicting Causative Regulatory Variants
- Other studies
- GUANinE v1.1 reveals complementarity of supervised and genomic language models
- Rule
- Ordering reversal; different tasks across studies
- Model identity
- Matched across studies partly through links not yet reviewed
The compared numbers
Each value links to its result record and source. Compare values within one protocol; the rule compares rankings, not raw values across protocols.
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.
Finding discrepancy-9e66c51233d909ab · All contested results · How findings are made and followed up