Welcome !

News 2019 (Congratulations!)

Dr. Sora Yoon's paper on biclustering analysis is accepted for publication in Nucleic Acids Research.

Dr. Hai Nguyen got the 'new faculty's grant' (2 years) about deep neural network based analysis of single cell RNA-seq data from National Research Foundation.

News 2018 
(Congratulations!)

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 Bronze Prize from the 1st K-Genome competition (GScluster software).



Bioinformatics Lab at UNIST is led by prof. Dougu Nam.

Research interests :

(1) Identifying molecular markers and their functional networks that are associated with disease by analyzing transcriptomic and genomic big data
(2) Developing mathematical/statistical models, algorithms and software for analyzing high-throughput genome data
(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. 

In particular, we develop and make use of gene set analysis methods to identify robust pathway-based signatures and regulatory networks, robust clustering algorithms to classify subtypes of disease and reverse-engineer the intracellular networks of RNAs and proteins. 

We also use machine learning methods for data clustering, dimension reduction, and classification.

 

Current Topics of Interest

Biclustering analysis of transcriptome big data

Pathway and network analysis of GWAS data
Robust and powerful meta-analysis methods development for bio big data analysis

Detection of rare drivers in cancer by integrating mutation and expression

Effective single-cell big data clustering using deep-learning

Protein interaction network weighted pathway analysis methods

Comparative study of RNA-seq differential expression analysis

Improving miRNA target prediction


Applicants are expected to

1. Have strong interest in computational approach to biology and disease

2. Enjoy programming whatever his/her level is

OR

3. Show excellency or desire to be excellent(!) in any of biology, statistics/mathematics, and programming

 
Contact: prof. D. Nam (dougnam@unist.ac.kr)