High-throughput technology such as DNA sequencing could produce massive data from clinical samples. The research of Liguo Wang, Ph.D., concentrates on developing computational tools and methods to transform big data into biological insights and provide cognitive support for data-informed clinical decision-making and precision medicine.
High-throughput sequencing data analysis, mining and integration. The amount of genomic data is growing exponentially and spreads over numerous heterogeneous data repositories. Dr. Wang's research focuses on developing new bioinformatics solutions or applying existing tools to analyze, integrate and interpret omics data, including:
- Whole-genome sequencing
- Whole-exome sequencing
- RNA sequencing (RNA-seq)
- Whole-genome bisulfite sequencing
- Reduced representation bisulfite sequencing
- Chromatin immunoprecipitation sequencing (ChIP-seq)
- Assay for transposase-accessible chromatin using sequencing (ATAC-seq)
- Single-cell sequencing data
- Machine learning, artificial intelligence and genomic medicine. Most human diseases have a molecular basis. Dr. Wang's research focuses on using computational approaches to identify genomic, transcriptomic and epigenomic aberrations underlying human diseases, particularly cancers. His research goals include identifying stand-alone or composite biomarkers that are causal or associated with certain diseases or traits — such as cancer progression or therapeutic resistance — and using machine learning approaches to build descriptive or predictive models based on genomic and clinical data.
Significance to patient care
Big data promises precision medicine, also called individualized or personalized medicine. Integrating heterogeneous molecular and clinical data in a holistic manner fulfills data-informed disease management and personalized medicine.
- Associate editor, BMC Bioinformatics, 2014-present