Domain Adaptation and Generative Models for Single Cell Genomics with Gerald Quon - TWiML Talk #251

This article summarizes a discussion with Gerald Quon, an assistant professor at UC Davis, about his work on deep domain adaptation and generative models for single-cell genomics. The focus is on how these techniques are used to identify diseases for treatment purposes. The conversation covers the application of deep learning to generate new insights across different diseases, the types of data used, and the development of nested generative models for single-cell measurements. The article highlights the potential of AI in advancing medical research and disease treatment through the analysis of genomic data.

• •Gerald Quon's research focuses on using deep learning for single-cell genomics.
• •The work explores domain adaptation and generative models for disease identification.
• •The research aims to generate novel insights across diseases and improve treatment.