Silvia (Shuchang) Liu, PhD (University of Pittsburgh)
Topic: Computational multi-omics data analysis in biomedical applications
Computational genomic data analysis involves using computer algorithms and statistical techniques to analyze vast amounts of genetic information obtained from sequencing and other high-throughput technologies. Multi-omics refers to the integration and analysis of data from multiple "omics" technologies, such as genomics, transcriptomics, proteomics, metabolomics, and epigenomics, to provide a more comprehensive understanding of biological systems. This presentation will first briefly introduce the concept of computational multi-omics data integration and followed by two biomedical applications. In the first application, the cutting-edge spatial transcriptomic technology will be introduced. As an example, investigation on liver zonation will be presented to illustrate how this innovative approach to map the spatial distribution of gene expression within tissue samples. In the second application, a multi-omics machine learning model will be introduced, which is able to predict the likelihood of hepatocellular carcinoma recurrence after liver transplantation based on transcriptome and whole‐exome sequencing analyses.
Dr. Liu is an Assistant Professor from University of Pittsburgh School of Medicine. She is currently serving as the Co-Director of the Genomics and Systems Biology Core of Pittsburgh Liver Research Center. Dr. Liu received her PhD in the Joint CMU-Pitt Ph.D. Program in Computational Biology in 2017. She has been working in the field of bioinformatics and biostatistics genomic data analysis. She has published more than 90 papers and abstracts with more than 1700 citations based on Google Scholar.
Dr. Liu’s research interests focus on developing novel computational models and application into diverse collaborative works. For methodological development, she is interested in computational analysis on both bulk and single-cell long-read RNA-seq data to detect fusion transcripts and mutation isoforms. In addition, she has been working on developing novel machine learning algorithms to perform meta- and integrative- genomic data analysis. For application works, she has been collaborating with different labs for bioinformatics and biostatistics genomic data analysis. These collaborative projects also promote innovative ideas for her methodology works.