Deep learning shapes single-cell data analysis

Qin Ma Speaker
The Ohio State University
Wednesday, Aug 9: 8:35 AM - 8:55 AM
Topic-Contributed Paper Session 
Metro Toronto Convention Centre 
Artificial intelligence (AI) and single-cell studies have been making waves in the science and technology communities. AI offers a broad range of methods that can be used to investigate diverse data- and hypothesis-driven questions in single-cell biology (Ma, Q., Xu, D. Deep learning shapes single-cell data analysis. Nat Rev Mol Cell Biol, 2022). The highly heterogeneous nature of single-cell data can be analyzed across a wide range of research topics by generalizing deep-learning model design and optimization in a hypothesis-free manner. This talk will introduce in-house graph representation learning methods for gene expression data to discover underlying mechanisms in diverse biological systems.