Multimodal Learning Reveals Plants' Hidden Sensory Integration Logic
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2025
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| _version_ | 1866902257749983232 |
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| author | VOMO DONFACK, Kelly Larissa González Doblas, Verónica Morilla, Ian |
| author_facet | VOMO DONFACK, Kelly Larissa González Doblas, Verónica Morilla, Ian |
| contents | <p>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:</p> <ul> <li> <p><strong>Biologically Guided Design</strong>: Hardcodes domain knowledge through:</p> <ul> <li> <p><em>BiologicalPriorLayer</em> (upweights known functional genes/metabolites)</p> </li> <li> <p><em>PathwayGuidedAttention</em> (models cross-modal interactions)</p> </li> </ul> </li> <li> <p><strong>Multimodal Fusion</strong>: Combines four data types using contrastive learning (NTXentLoss) and cross-modal attention.</p> </li> <li> <p><strong>State-of-the-Art Performance</strong>: Achieves 0.98 F1 score and 0.99 AUROC, outperforming unimodal baselines by 15-35%.</p> </li> <li> <p><strong>Interpretability</strong>: Provides attention matrices and SHAP values for biological insights.</p> </li> </ul> <p>The repository includes:</p> <ul> <li> <p>Pretrained PyTorch model (<code>*.pt</code> file)</p> </li> <li> <p>Training/evaluation scripts (<code>train.py</code>)</p> </li> <li> <p>Example notebooks for visualization</p> </li> <li> <p>Configuration files for reproducibility</p> </li> </ul> <p><strong>Applications</strong>:</p> <ul> <li> <p>Plant-pathogen interaction studies</p> </li> <li> <p>Multimodal biomarker discovery</p> </li> <li> <p>Interpretable ML for biological systems</p> </li> </ul> <p><strong>Technical Details</strong>:</p> <ul> <li> <p>Framework: PyTorch Lightning</p> </li> <li> <p>Language: Python 3.8+</p> </li> <li> <p>License: MIT</p> </li> </ul> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_16281076 |
| institution | Zenodo |
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| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Multimodal Learning Reveals Plants' Hidden Sensory Integration Logic VOMO DONFACK, Kelly Larissa González Doblas, Verónica Morilla, Ian Machine learning multimodal integration plant-microbe interactions interpretable AI PyTorch biological prior plant systems biology <p>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:</p> <ul> <li> <p><strong>Biologically Guided Design</strong>: Hardcodes domain knowledge through:</p> <ul> <li> <p><em>BiologicalPriorLayer</em> (upweights known functional genes/metabolites)</p> </li> <li> <p><em>PathwayGuidedAttention</em> (models cross-modal interactions)</p> </li> </ul> </li> <li> <p><strong>Multimodal Fusion</strong>: Combines four data types using contrastive learning (NTXentLoss) and cross-modal attention.</p> </li> <li> <p><strong>State-of-the-Art Performance</strong>: Achieves 0.98 F1 score and 0.99 AUROC, outperforming unimodal baselines by 15-35%.</p> </li> <li> <p><strong>Interpretability</strong>: Provides attention matrices and SHAP values for biological insights.</p> </li> </ul> <p>The repository includes:</p> <ul> <li> <p>Pretrained PyTorch model (<code>*.pt</code> file)</p> </li> <li> <p>Training/evaluation scripts (<code>train.py</code>)</p> </li> <li> <p>Example notebooks for visualization</p> </li> <li> <p>Configuration files for reproducibility</p> </li> </ul> <p><strong>Applications</strong>:</p> <ul> <li> <p>Plant-pathogen interaction studies</p> </li> <li> <p>Multimodal biomarker discovery</p> </li> <li> <p>Interpretable ML for biological systems</p> </li> </ul> <p><strong>Technical Details</strong>:</p> <ul> <li> <p>Framework: PyTorch Lightning</p> </li> <li> <p>Language: Python 3.8+</p> </li> <li> <p>License: MIT</p> </li> </ul> |
| title | Multimodal Learning Reveals Plants' Hidden Sensory Integration Logic |
| topic | Machine learning multimodal integration plant-microbe interactions interpretable AI PyTorch biological prior plant systems biology |
| url | https://doi.org/10.5281/zenodo.16281076 |