◆ Computational analysis of biological networks
We develop novel algorithms and software tools for biological network analysis. Biological activities can be described as network-based activities between various biological entities, thus using network models to represent and analyze biological systems provides multiple advantages. Our aim in this research is understanding biological contexts based on network models and devising novel algorithms to infer intervention strategies for altering contexts.
We focus on various topics including, but not limited to:
- Condition-specific pathway identification
- Individualized pathway activity quantification
- Pathway-based disease subtyping
- Drug-pathway association analysis
- Dynamic modeling of disease intervention
◆ Developing the web-based software platform for inferring diagnosis of neurogenetic
disorders based on integrated data analysis.
We have developed a web-based diagnostic assistant software platform (NeuroScanDx) in order to assist the diagnosis of neurogenetic disorders. NeuroScanDx recommends predicted diagnosis of patients with the supporting evidences of disease candidates from the multidimensional data (e.g., genomic data, phenotypic data, and MRI).
We focus on developing methods for prioritizing disease candidates of a patient:
- Genetic variant evaluation
- Phenotypic data evaluation
- MRI Image evaluation
- Case-to-Evidence similarity evaluation (e.g., Case-to-Evidence similarity is calculated
by integrating the evaluated similarity score of phenotypic, genetic variant, MRI image)
◆ Systematic interpretation of disease
Discovering various genomic characteristics in cancer samples
Various genomic alteration cause cancer and affect cancer progression. Using data from NGS sequencing, we analyze genomic characteristics of tumor and normal samples to discover tumor specific genomic alteration. We also study cancer progression by analyzing genomic alteration at specific cancer stages.
We focuses on the following genomic alterations :
1.Detecting somatic mutation
2.Identifying expression level
3.Detecting copy number variation
4.Identifying changes of microRNA regulation
Systematic interpretation of disease on microbiome
Identifying microbial communities from human microbiome gene sequencing data. We investigate the taxonomic differences by analyzing microbial sequencing data from healthy person or patient.
Our aim is identifying the disease-related microbiome from patients by using various analysis approaches.
Identification of state-transitional biology from healthy conditions to obesity and type2 diabetes
We will identify state-transitional biology from healthy conditions to obesity and type 2 diabetes(T2DM) of adipose tissue, liver tissue, and muscle tissue.
- Genetic expression data of adipose tissue, liver tissue, and muscle tissue, which are major organs related to metabolic diseases, are analyzed at gene and pathway level.
- We focus on genes that play a particularly important role at
gene level and pathway level.
• Identifying Differential Expressed Genes(DEGs) of state-
• Analysis and Visualizing of Differential Expressed Genes