Title: Continuous lifelong learning for modelling of gene regulation from single cell multiome data by leveraging atlas-scale external data Speaker: Dr Zhana Duren (Clemson University) Abstract: Inferring context-specific Gene Regulatory Networks (GRNs) from genomics data is a crucial task in computational biology. However, the accuracy of inferred GRNs is often low due to the limitations of current methods. We developed a method called scPECA, which infers gene regulation from single cell Paired gene Expression and Chromatin Accessibility data from the same cell. We also propose a metric called the "pioneer index" which aims to improve the accuracy of GRN and interpretability of the model by providing a quantitative measure of the TFs’ ability to initiate chromatin remodeling. The scPECA method achieved 3 times higher accuracy compared to currently available GRN inference methods when ChIP-seq data was used as the ground truth. We found that disease genes from both differential expression analysis and Genome-Wide Association Studies (GWAS) were enriched in the target genes of TFs with high pioneer index scores. About the speaker: Dr Zhana Duren earned his BS in Mathematics and Applied Mathematics from Beihang University (China) in 2012. He was awarded his PhD in Operational Research and Cybernetics from Academy of mathematics and systems science, Chinese Academy of Sciences. From 2015-2020, he worked in Professor Wing Hung Wongâs lab at Stanford University as a visiting PhD student (2015-2017) and postdoc research fellow (2017-2020). He is currently Assistant Professor in the Department of Genetics and Biochemistry at Clemson University.