Learning Cell-Aware Hierarchical Multi-Modal Representations for Robust Molecular Modeling
1. A new framework called CHMR is proposed to address limitations in current cell-aware molecular modeling approaches, focusing on modality incompleteness and insufficient hierarchical modeling across molecular, cellular, and genomic levels.
2. CHMR introduces a tree-structured vector quantization module to capture latent biological hierarchies, enabling the model to better understand cross-scale biological mechanisms from molecules to cells and genes.
3. The framework incorporates modality augmentation and semantic consistency alignment to handle missing biological data, significantly improving robustness and generalization in molecular property prediction tasks.
4. Evaluated on nine public benchmarks with 728 tasks, CHMR outperforms state-of-the-art methods, achieving average improvements of 3.6% in classification and 17.2% in regression tasks.
5. CHMR's hierarchical and multi-modal learning approach provides a generalizable framework for integrative biomedical modeling, offering reliable and biologically grounded molecular representations.
📜Paper:
arxiv.org/abs/2511.21120v1
#MolecularModeling #HierarchicalLearning #MultiModalRepresentations #BiomedicalModeling