This research introduces a novel framework that combines language models with biological experimentation to efficiently explore perturbation spaces and extract meaningful insights.

• Integrates semantic biological knowledge into machine learning pipelines
• Aligns computational objectives with downstream biological analyses
• Reduces experimental costs while maintaining scientific rigor
• Enables more targeted and efficient exploration of complex biomolecular systems

Why It Matters: This approach has the potential to dramatically accelerate biological discovery by making high-content perturbation experiments more accessible and interpretable, leading to faster insights in drug discovery and disease understanding.

Contextualizing biological perturbation experiments through language