Detalle de: Multimodal Learning Reveals Plants' Hidden Sensory Integration Logic

CoMM-BIP (Contrastive Multimodal Model with Biological Informed Priors) is an interpretable deep learning framework that integrates transcriptomic, metabolomic, phenomic, and environmental data to predict multi-sensory effector responses in plants. Key features: Biologically Guided Design: Hardcodes domain knowledge through: BiologicalPriorLayer (upweights known functional genes/metabolites) PathwayGuidedAttention (models cross-modal interactions) Multimodal Fusion: Combines four data types using contrastive learning (NTXentLoss) and cross-modal attention. State-of-the-Art Performance: Achieves 0.98 F1 score and 0.99 AUROC, outperforming unimodal baselines by 15-35%. Interpretability: Provides attention matrices and SHAP values for biological insights. The repository includes: Pretrained PyTorch model (*.pt file) Training/evaluation scripts (train.py) Example notebooks for visualization Configuration files for reproducibility Applications: Plant-pathogen interaction studies Multimodal biomarker discovery Interpretable ML for biological systems Technical Details: Framework: PyTorch Lightning Language: Python 3.8+ License: MIT