Towards an interpretable deep learning model of cancer cells.
April 25, 2025 @ 10:00 – 11:00 CEST
Avlant Nilsson, DDLS fellow, Karolinska Institutet
NBIS and SciLifeLab Data Centre arrange an open SciLifeLab AI Seminar Series aimed at knowledge-sharing about Artificial Intelligence and applications in the Life Science community. The seminar series is open to everyone. The seminar is run over Zoom on the third Friday of the month during academic terms, typically between 10 and 11 am, with approx. 45 min presentation and 15 min discussion.
Abstract
Cancer emerges from complex molecular interactions that drive pathological cells states. To model these interactions, we develop deep learning frameworks of cellular signaling, gene regulation, and metabolism. Our approach embeds prior knowledge networks into a recurrent architecture, allowing us to capture cellular dynamics by training on omics data across different perturbations and conditions.
At the core of our modeling is a propagator function that predicts the next cell state based on the current state, enabling the simulation of molecular state transitions over time. To ensure biological plausibility, we employ a technic based on feature embedding and superposition that encodes molecular identities in a structured feature space while restricting the predictive input for each molecular state to its direct network connections. These structured embeddings facilitate the use of a universal function allowing generalization across different molecules, cell types, and experimental settings.
By constraining learned representations to known molecular entities, our framework maintains interpretability while enabling data-driven inference. Ultimately, we aim to integrate our models into a unified representation of cellular behavior. Bridging mechanistic understanding with predictive modeling, this work lays the foundation for AI-driven precision medicine, offering new tools to simulate and control cancer cell states.
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