Faculty Research Profile
Dougu Nam
학력
· 2002. 8: KAIST, Applied Math, MS/Ph.D.
· 1996: Seoul National University, Math. Edu. B.S.
주요 경력
Senior Researcher, Korea Research Institute of Bioscience and Biotechnology (Bioinformatics) Senior Researcher, National Institute for Mathematical Sciences (Bioinformatics)
수상/학회/외부활동
· 2011~present: Review Editor of Frontiers in Genetics/Physiology (IF 3.5/3.2)
· 2011~present: Review Editor in Frontiers in Genetics/Physiology (IF 3.5/3.2)
· 9th European Conference on Computational Biology, oral presentation(Sept. 2010, Ghent, Belgium)
· Workshop on Integrative and Systems Biology (2 days), organizer (Dec. 2011, UNIST)
생물정보학 연구실
Bioinformatics Lab
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
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
연구주제
Bioinformatics, Genomics, Systems biology, Single-cell data, Transcriptomics, GWAS, microRNA
Bioinformatics, Genomics, Systems biology, Single-cell data, Transcriptomics, GWAS, microRNA
국가연구개발사업 기술 분류체계
국가과학기술표준분류
LA. 생명과학 > LA07. 융합바이오 > LA0706. 생물정보학
논문
· 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)