The interest of our lab is genetics, genomic and human disease. We focus on developing computational methods to better understand transcriptional regulation underlying biological processes and human diseases. To facilitate method development, we choose three levels of models- cell cycle regulation for pathway level, stem cell for cell level, and breast cancer for disease level.

More specifically, our research interests include:

(1) Construct integrated regulatory network underlying cancer and other diseases

Biological processes are precisely regulated by TFs, miRNAs and other regulators. We aim to combine data from different sources, e.g, TF binding data (ChIP-seq and ChIP-chip), gene expression data (microarray and RNA-seq), with computational techniques to construct the integrated regulatory networks underlying cancer and other diseases.



(2) Infer combinatorial interactions among TFs under different conditions

TFs are collaborated with each other during transcriptional regulation. We aim to infer the combinatorial interactions among different TFs. We also aim to investigate how TF-TF interactions vary in different cell types/conditions, and how these interactions evolve in different related species.


(3) Epigenomics of transcriptional regulation in cancer and other diseases

Biological processes are also under intensive regulation at the epigenomic level. We aim to understand the functions of DNA methylation and histone modifications in these processes and to provide new insight on the relationship between epigenetic events and human diseases, e.g. tumor development and progression.

 

(4) Tumor classification and clinical outcome prediction

Strictly speaking, each case of cancer should be regarded as a specific disease, but cancer of the same type can often be categorized into different sub-types. We aim to develop methods that integrate different data sources (e.g. expression data, somatic mutation profile, epigenetic data, etc) to classify tumor. We also aim to predict the clinical outcome of patients based on the molecular features of tumors and their genotype.


Last updated: Jun 1, 2013