Welcome !
News 2023
Dr. Bukyung Baik received "Excellent paper award" from Korean Bioinformatics Society.
Dr. Hai Nguyen and Dr. Bukyung Baik's paper on single-cell data integration was published in Nature Communications.
News 2021
Sounkou published an article on miRNA regulation in nonalcoholic fatty liver disease in eLife.
Dr. Yoon and Bukyung published novel meta-analysis methods in Scientific Reports.
News 2020
We received a new mid-career grant and genome information utilization grant.
Bukyung's RNA-seq analysis benchmarking paper was published in Plos One.
Dr. Sora Yoon went to UPenn for post-doc research.
Jinhwan's netGO paper was published in Bioinformatics.
News 2019
Sora and Jinhwan's paper on GScluster was published in BMC Genomics.
Prof. Nam addressed a highlight talk in CNB-MAC 2019 (US).
Prof. Nam chaired two sessions in ACM-BCB 2019 (US).
Dr. Sora Yoon's paper on biclustering analysis was published in Nucleic Acids Research.
Dr. Hai Nguyen received the new researcher's grant (2 years) about deep learning based analysis of single cell RNA-seq data from National Research Foundation.
News 2018
Prof. Nam addressed a keynote talk in 16th Korea-China-Japan Bioinformatics symposium (Japan).
Sora and Hai published GSA-SNP2 paper in Nucleic Acids Research.
Juok received Global PhD Fellowship (GPF) grant and will be supported for the next three years (2018 - 2020).
Sora received Excellent Poster Awards from TBC/BIOINFO2018 conference (Seoul) and Post-Genome Workshop (Yeosu) (Biclustering analysis).
Jinhwan received Participation Prize from the 1st K-Genome competition (GScluster software).
Graduate student positions are open.
Students who majored in biology/bioengineering, computer science, math/statistics and other related fields are eligible.
Contact: Prof. Dougu Nam (남덕우 dougnam@unist.ac.kr)
Research interests :
(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, classification, and pathway analysis methods
Identification of disease-specific miRNA regulatory networks from gene expression data
Biclustering analysis of transcriptome big data
Detection of rare drivers in cancer by integrating mutation and expression
Read count modeling and simulation of RNA-seq and single cell data
