Where one study disagrees with what other studies report for the same models, and what we are doing to resolve each case.
These are automatic leads, not findings of error. Each release, a check compares how every study ranks the models it shares with other studies. It flags a study that reverses the order the others agree on, or whose gap to a shared reference model lies well outside theirs. Different tasks, populations or metrics often explain a flag.
A flag never hides a result. Flagged results stay in every table, page and download, marked Contested. A result is withheld only when a reviewer marks it disputed.
GUANinE v1.1 reveals complementarity of supervised and genomic language models ranks Caduceus above ChromBPNet in 6 of 6 comparisons; Comparative Analysis of Deep Learning Models for Predicting Causative Regulatory Variants ranks ChromBPNet above Caduceus in 4 of 4 comparisons. No other study gives a verdict, so neither is the outlier.
2 papers rank ChromBPNet above Caduceus in 4 of their 10 comparisons and Caduceus higher in 6. Of those papers, GUANinE v1.1 reveals complementarity… ranks Caduceus higher on its own (6 of 6).
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.
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).