Machine Learning via Graph Signal Processing for Complex Biological Data

Explore machine learning techniques for analyzing complex biological data through graph signal processing in this conference talk from the Computational Genomics Summer Institute. Delve into the foundations of graph signal processing and its applications in computational biology. Learn about geometric scattering for graph data analysis and data-driven learning of geometric scattering networks. Discover how these advanced techniques can be applied to complex biological datasets, potentially revolutionizing our understanding of genomic and molecular data. Gain insights into the latest research in this field, including related works on algebraic signal processing theory and its applications to biological systems.

Smita Krishnaswamy | Machine Learning via Graph Signal for Complex Biological Data | CGSI2023