Deep Neural Network Models for Inference of Cellular Dynamics and Underlying Regulatory Networks - Granger Causality and RITINI

Explore deep neural network models for inferring cellular dynamics and underlying regulatory networks in this 27-minute conference talk from the Computational Genomics Summer Institute. Learn about advanced computational approaches including Granger causality methods and RITINI (Regulatory Inference Through Integrated Neural Information) for analyzing cellular processes. Discover how these sophisticated machine learning techniques can be applied to understand complex biological systems, decode regulatory mechanisms, and predict cellular behavior patterns. Gain insights into cutting-edge methodologies that bridge computational genomics with deep learning to advance our understanding of cellular dynamics and gene regulatory networks.