The primary focus of the Li Lab is on assessing the performance of next-generation sequencing techniques in accurate genome/epigenome/transcriptome profiling and understanding the clinical and functional role of epigenome heterogeneity in the cancer evolution. The Li laboratory utilizes computational and sequencing methodologies to identify and characterize the essential epigenetic lesions that guide cancer cells to evolve and escape from therapy. Our research interest is to understand the inner workings of cancer cells – the genetic and epigenetic heterogeneity that drive cancer initiation and progression. Specifically, the research involves (1) determining the drivers of epigenetic heterogeneity; (2) evaluating the functional impact of the cross-talk among epigenetic modifications on transcriptome; (3) assessing epigenetic heterogeneity/subpopulations in treatment resistance.
Dr. Li has developed a series of computational methods and software for the epigenome sequencing data analysis, to comprehensively detect the significant DNA methylation aberration and epigenetic heterogeneity during disease progression. Dr. Li's work has helped to establish the first principles and metrics for examining changes in RNA splicing and expression profiling and set standards at the FDA for clinical-grade RNA-sequencing. Dr. Li further applied these approaches to study the epigenetic heterogeneity and dynamics using acute myeloid leukemia (AML) as a model. Dr. Li found that epigenetic allele burden was linked to inferior clinical outcome, and epigenetic dynamics was related to hypervariable transcriptional regulation and was divergent from the genetic burden.
I am interested in applying machine learning and advanced statistical modelling into biological questions.
I am interested in epigenetic pattern recognition of cancer, biomedical big data mining, next-generation sequencing data pattern mining and...
Developing novel and advanced deep learning algorithms to analyze single-cell epigenome data.
What if, much like screening through genetic testing, we could learn how to better treat individual patients through their...
Modifications of the genome that do not change the sequence of the DNA, called epigenetic modifications, can impact how genes are expressed...
Machine learning, Translational bioinformatics, Big data, Deep learning,
Applies machine learning and text mining techniques to the analysis and curation of the Human Phenotype Ontology and other...
Engineering the human skin microbiome to treat diseases and prevent infections.
Investigates the key genomic change in cancers and reproduce and demonstrate the effect in vitro/vivo
Investigate cancer evolution using computational and statistical approaches.
I am interested in heterogenous nature of cancer, which I am going to study from the epigenomic perspective.
Develops software that implements statistical and algorithmic approaches to analyze and integrate immunoprofile and...
Employs genomic and computational approaches to investigate how the immune response shapes brain tumor development.
Interested in genetic diversity and evolution of wild and wild-derived mice
Unraveling the patterns of genetic mechanisms that give rise to genetic diversity in house mouse populations.
My research interest is to understand the infrastructure of chromatin interactions.