Explain Neural Networks using Layer-Wise Relevance Propagation and evaluate the explanations using Pixel-Flipping and Area Under the Curve.
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Updated
Aug 7, 2022 - Python
Explain Neural Networks using Layer-Wise Relevance Propagation and evaluate the explanations using Pixel-Flipping and Area Under the Curve.
Python code to obtain metrics like receiver operating characteristics (ROC) curve and area under the curve (AUC) from scratch without using in-built functions.
How to cite: Van De Vyver, A.J., Eigenmann, M.J., Ovacik, M., Pöhl, C., Herter, S., Weinzierl, T., Fauti, T., Klein, C., Lehr, T., Bacac, M., Walz, A.-C. (2021). A novel approach for quantifying the pharmacological activity of T cell engagers utilizing in vitro time course experiments and streamlined data analysis. AAPS
AUCC (Python Implementation)
An innovative solution for finding the area under the curve for hand-drawn graphs
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