LIME
Definition
LIME (Local Interpretable Model-agnostic Explanations) is an explainable AI technique that interprets predictions from complex machine learning models by approximating them locally with simpler, interpretable models. In biological research, LIME helps researchers understand which features (genes, proteins, molecular descriptors) most influence model predictions for specific instances, such as disease classification or drug response prediction. By perturbing input data and observing prediction changes, LIME generates explanations showing feature importance for individual predictions. This is crucial in life sciences where model interpretability is essential for validating biological relevance, generating hypotheses, and ensuring regulatory compliance in clinical applications.
Visualize LIME in Nodes Bio
Researchers can visualize LIME explanations as network graphs where nodes represent features (genes, proteins, pathways) and edge weights indicate their contribution to specific predictions. This network view reveals feature interactions and dependencies that influence model decisions. Users can overlay LIME importance scores on biological networks to identify key regulatory nodes, compare explanation patterns across different samples, and trace how perturbations propagate through molecular interaction networks to affect predictions.
Visualization Ideas:
- Feature importance networks showing genes/proteins ranked by LIME scores with biological interaction edges
- Comparative networks displaying LIME explanations across different patient samples or experimental conditions
- Integrated networks combining LIME feature weights with pathway annotations and protein-protein interactions
Example Use Case
A pharmaceutical team develops a deep learning model predicting patient response to immunotherapy based on gene expression profiles. Using LIME, they explain why the model classified a particular patient as a non-responder. The LIME analysis reveals that low expression of three immune checkpoint genes and elevated expression of immunosuppressive pathway components drove this prediction. By visualizing these features as a network with their known protein interactions, researchers identify a novel combination therapy target that could overcome resistance mechanisms in this patient subgroup.