Our research primarily focuses on advancing statistical and bioinformatics methodologies to enhance cancer research and medical treatment. We have developed and co-developed multiple bioinformatics tools available in Biostat Wiki.
We are collaborating with several international and national clinical and systems-biology collaborators as well as bioinformatics research groups. Currently, our main research activities are in several areas:
Methodologies to analyze high-throughput omics data: We have developed multiple methods for different types of omics data: gene/isoform quantification from RNA-sequencing and metagenomics data, detection of abnormal RNAs including gene fusion and circular RNAs, analysis of NMR spectroscopy in metabolomics.
Single-cell sequencing analysis: Our research extends to the analysis of single-cell RNA-sequencing (scRNA-seq) data including model fitting and differential expression analysis, isoform expression pattern discovery, cell-level mutation detection, and isoform quantification for tag-based scRNA-seq data.
Characterizing and modeling cancer omics: discovery of driver alterations in cancer, identification of subtype-specific genes, particularly in breast cancer and Acute Myeloid Leukemia (AML), and exploration of mutant-allele expression in cancer.
Pharmacogenomics: We are focusing on exploitation of available public (and in house) pharmacogenomics data to explore responses of drugs and their potential mechanisms. We have developed predictive models for both monotherapy and drug combinations, focusing on applications in AML. We are recently interested in drug repurposing and personalized drug repurposing using pharmacogenomics data.