Research Interests

We are computational biology lab! Our lab is focused on development of statistical and computational algorithms to explore genetic and epigenetic regulation in cancer and other diseases from high-throughput sequencing data. We attempt to dissect the upstream and downstream regulatory mechanisms of deregulated genetic and epigenetic elements involved in the development and progression of tumors, by integrating multidimensional omics data including somatic mutation profiles, copy number variation profiles, expression profiles and epigenetic modifications profiles at genomic, transcriptional, post-transcriptional and epigenetic levels. 

Current Projects

1. Precise identification of molecular biomarkers for cancer diagnosis, prognosis and therapeutic intervention
a. Computational identification of epigenetically-regulated cancer driver targets and genome-wide CRISPR screening validation.
b. Functional exploration  of ncRNAs in cancer-immune system interactions.
2. Artificial Intelligence and Biostatistical algorithms development for medical diagnosis
a. Development of high-throughput sequencing data (ChIP-Seq, BS-Seq, RNA-Seq and Hi-C) platform and statistical methods for identify clinic relevant biomarkers of cancer.
b. Development and application of Artificial Intelligence algorithm in medical diagnosis and treatment.
c. Development of statistical methods and algorithms for single-cell DNA methylation and RNAseq.
3. Evolution and allelic architecture of complex disease: investigate the impact of common and rare genetic variants to both the non-Mendelian and Mendelian phenotype
The field of human genetics has been leveraged by large-cohort based genome-wide association studies (GWAS), which identified thousands of genomic regions containing polymorphisms/variants that influence a wide variety of disease trait. These disease-associated loci have provided tremendous novel clues for disease biology and molecular mechanisms. We are enthusiastically working on building causal networks linking specific genetic loci to disease phenotypes (e.g. scoliosis and comorbidities).

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