Cyrille Delpierre

The paper introduces a visualization method to analyze SHAP feature interactions in ML models for biomedical applications, revealing hidden patterns of explainability.

This paper introduces discernibility as a metric to evaluate the clarity of explanations in machine learning models for medicine, finding that the dcor metric best captures user-perceived relationships between data and explanations.

The paper shows that maternal separation in C3H/HeN mice increases motivation for palatable food and alters nucleus accumbens gene expression, while C57Bl/6J mice show no such changes.