Do Perturbation Predictors Learn Gene Interactions? Diagnosing Extrapolation in Agentic Screening

Z. Gao, A. Author, B. Author, C. Author, D. Author

gene interactionsmachine learningperturbation screeningpredictive modeling

2610.18880

Builder Relevance

70%

2h ago

Abstract

The paper investigates the limitations of machine-learning models in predicting gene interactions that have not been directly measured.

Reality Card

Core Claim

The study demonstrates that a simple additive prediction method can outperform specialized neural predictors in terms of consistency and reliability when predicting unseen gene combinations.

Method / Result

Additive disagreement (AD) is shown to be a consistent and informative measure across all evaluated datasets.

Limitations

The conventional confidence signals used in the models are inconsistent across datasets and models, which raises concerns about reproducibility.

Paper to code

Verified implementation resources so builders can test the paper’s claims instead of stopping at the abstract.

No verified implementation link has been attached yet. AIBuzzHub will keep this panel separate from unverified search results.

← Back to all papers