第二十九期

l 2022年12月9日（周五） zoom会议：881 5801 8511 密码：514529    

报告43：

报告时间：2022年12月9日（周五）11:00-13:00

报告嘉宾：Dr. Qin Ma

Zoom 链接:https://us06web.zoom.us/j/88158018511?pwd=L2Z1bDZESTFCKzJYZWduYVhWenYvZz09

报告题目：Graph Representation Learning of Gene Expression Data

报告摘要：

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.

报告人简介：

Qin Ma is currently an associate professor and the Chief of the Bioinformatics and Computational Biology Section in the Department of Biomedical Informatics, Ohio State University (OSU), and Leader of the Immuno-Oncology Informatics Group Pelotonia Institute for Immuno-Oncology at The OSU Comprehensive Cancer Center. He received his Ph.D. in Operational Research from Shandong University and then did his postdoc at the University of Georgia, specializing in high-throughput sequencing data mining and modeling. He established his bioinformatics research lab and moved on to the field of single-cell sequencing data analyses at South Dakota State University. Currently, his lab focuses on developing computational methods to discover heterogeneous transcriptional regulatory mechanisms from single-cell multi-omics data, with a particular interest in deploying deep learning methods in cancer research.

Lab website: https://u.osu.edu/bmbl/.