GEARS

KG-guided prediction: Combines deep-learning models with an explicit gene-relationship knowledge graph, letting the model leverage structured biological priors.

Combinatorial perturbations: Predicts cellular responses to combinations of perturbations, a harder regime than single-perturbation prediction.

40% precision gain: Achieves 40% higher precision than prior approaches when predicting four distinct genetic-interaction subtypes in a combinatorial perturbation screen.

Drug discovery relevance: Accelerates hypothesis generation in perturbation biology, with direct implications for target discovery and drug development.