Statistical Bioinformatics Seminar: Cao

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.