New Machine Learning Models for Single-cell and Regulatory Genomics
Explore cutting-edge machine learning models for single-cell and regulatory genomics in this comprehensive lecture from the Computational Genomics Summer Institute. Delve into Christina Leslie's presentation on innovative approaches for analyzing single-cell multi-omics data, enhancer identification, and chromatin accessibility. Learn about new methodologies for embedding single-cell ATAC-seq data and predicting 3D contact maps from chromatin accessibility. Gain insights into the latest advancements in computational genomics, including functional and disease-associated enhancer identification, chromatin potential analysis, and scalable sequence-informed embedding techniques. Discover how these novel machine learning models are revolutionizing our understanding of gene regulation and cellular heterogeneity at the single-cell level.