Bioinformatics Lab

생물정보학 연구실

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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

Major research field

Desired field of research

Research Keywords and Topics

Systems biology
Single-cell data

Research Publications

Nature Communications, Benchmarking integration of single-cell differential expression, Hai C. T. Nguyen†, Bukyung Baik†, Sora Yoon, Taesung Park, Dougu Nam*, 14: 1570, (2023)

Nucleic Acids Research, Biclustering analysis of transcriptome big data identifies condition-specific microRNA targets, Sora Yoon, Hai C. T. Nguyen, Woobeen Jo, Jinhwan Kim, Sang-Mun Chi, Jiyoung Park, Seon-Young Kim, Dougu Nam*, 47(9), e53, (2019)

Nucleic Acids Research, Efficient pathway enrichment and network analysis of GWAS summary data using GSA-SNP2, Sora Yoon†, Hai C. T. Nguyen†, Yun Joo Yoo, Jinhwan Kim, Bukyung Baik, Sounkou Kim, Jin Kim, Sangsoo Kim and Dougu Nam*, 46(10), e60 (2018)


  • LA. 생명과학
  • LA07. 융합바이오
  • LA0706. 생물정보학