Title: Methods towards precision bioinformatics in single cell era Speaker: Dr Yue Cao (University of Sydney) Abstract: Single-cell technology offers unprecedented insight into the molecular landscape of individual cell and is transforming precision medicine. Key to the effective use of single-cell data for disease understanding is the analysis of such information through bioinformatics methods. In her PhD thesis, she examines and addresses several challenges in single-cell bioinformatics methods for precision medicine. First, the thesis discusses the challenges of single-cell bioinformatics and the recent success of deep learning and ensemble learning. It then introduces SimBench, a comprehensive framework for evaluating single-cell RNA-sequencing data simulation tools. It also presents scFeatures, an approach for creating interpretable molecular representations of individuals. Finally, the thesis applies scFeatures to multiple COVID-19 scRNA-seq data in a case study, demonstrating the impact of deep learning and ensemble learning on disease outcome prediction About the speaker: Yue Cao has recently completed her PhD in bioinformatics under the guidance of Prof. Jean Yang, A/Prof. Pengyi Yang and Dr. Shila Ghazanfar at the University of Sydney. She is currently a post-doctoral researcher at the Sydney Precision Data Science Centre at the University of Sydney. Her research interests revolve around the computational analysis of high-dimensional omics data in the era of precision medicine, with a particular focus on single-cell data.