We are bioinformatics group at UNIST.

Our research interests are as follows:
(1) Identifying molecular markers and their functional networks that are associated with disease by analyzing transcriptomic and genomic data
(2) Developing computational models and algorithms that impact bio-medical research
(3) Classifying disease subtypes or cell types using gene expression big data (microarray, RNA-seq, single cell)

To this aim, we analyze microarrays, RNA-seq, GWAS, and single cell data in an integrative manner.
We also use and develop machine learning methods for data processing, clustering, dimension reduction, and classification.

Current Topics of Interest:
Development of single-cell data processing, clustering, and classification methods
Biclustering analysis of transcriptome big data
Pathway and network analysis of gene expression and GWAS data
Detection of rare drivers in cancer by integrating mutation and expression
Read count modeling and simulation of RNA-seq and single cell data
Improving miRNA target prediction