D. Acosta-Alvear

Deep learning of dynamic signatures resolves mechanistic ambiguity in complex signaling networks

An AI-driven framework treating model dynamics as learnable signatures is introduced, using deep learning autoencoders to embed the dynamic signatures of thousands of competing models into a low-dimensional latent space and provides a rigorous, full-state alternative to scalar metrics.