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.
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.
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.
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.