explainability
Definition
Explainability refers to the ability to interpret and understand how computational models, particularly machine learning algorithms, arrive at their predictions or classifications in biological research. In life sciences, explainability is crucial for validating model decisions, identifying biological mechanisms, and building trust in AI-driven discoveries. It encompasses techniques like feature importance analysis, attention mechanisms, and pathway attribution that reveal which genes, proteins, or molecular features drive model outputs. Explainability bridges the gap between black-box predictions and actionable biological insights, enabling researchers to distinguish genuine biological signals from spurious correlations and generate testable hypotheses about disease mechanisms or drug responses.
Visualize explainability in Nodes Bio
Researchers can visualize model explainability through network graphs that highlight feature importance and causal relationships. Nodes Bio enables mapping of high-impact features identified by ML models onto biological networks, showing which genes or proteins most influence predictions. Edge weights can represent attribution scores, while node colors indicate positive or negative contributions, revealing interpretable pathways and molecular mechanisms underlying model decisions.
Visualization Ideas:
- Feature attribution networks showing genes/proteins ranked by model importance scores
- Pathway-level explainability maps highlighting enriched biological processes driving predictions
- Comparative networks displaying feature contributions across different patient cohorts or conditions
Example Use Case
A research team develops a deep learning model to predict patient response to immunotherapy in melanoma. While the model achieves 85% accuracy, oncologists need to understand why certain patients are classified as responders. Using explainability techniques, researchers identify that the model heavily weights specific immune checkpoint genes and interferon signaling pathways. By visualizing these high-attribution features as a network, they discover a novel gene signature involving STAT1, IRF1, and PD-L1 interactions that explains treatment sensitivity and suggests new combination therapy targets.