AROMA: Augmented Reasoning Over a Multimodal Architecture for Virtual Cell Genetic Perturbation Modeling
Virtual cell modeling predicts molecular state changes under genetic perturbations in silico, which is essential for biological mechanism studies. However, existing approaches suffer from unconstrained reasoning, uninterpretable predictions, and retrieval signals that are weakly aligned with regulatory topology. To address these limitations, we propose AROMA, an Augmented Reasoning Over a Multimodal Architecture for virtual cell genetic perturbation modeling. AROMA integrates textual evidence, graph-topology information, and protein sequence features to model perturbation-target dependencies, and is trained with a two-stage optimization strategy to yield predictions that are both accurate...
| Authors | Zhenyu Wang, Geyan Ye, Wei Liu et al. |
|---|---|
| Primary category | q-bio.QM |
| Published | 22 April 2026 |
| Last updated at source | 22 April 2026 |
Models and datasets referencing this paper
- blazerye/PerturbReasonDataset
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