Research topics in the post-genomic era

1. Drug Candidate Prediction


(1) Drug-drug interaction(DDI) prediction

Drug-drug interaction (DDI) is a change in drug effect when taken with the other drugs. Identification of DDIs is important in drug discovery since those interactions can cause antagonistic and toxic effects as well as synergistic. Moreover, combination therapy has been focused on because of its advantages on drug resistance and efficacy compared to monotherapy. Thus, we develop a computational model to predict DDIs using omics data and network analysis. Machine learning algorithms specifically classification algorithms are adopted to construct prediction models with calculated features from network analysis and each data type property information.

(2) Drug repositioning prediction

Drug repositioning is identifying new uses of existing drugs, which can contribute to a remarkable reduction in cost and time spent for traditional de novo drug discovery. Since drug repositioning deals with existing drugs which are already qualified their safety, it has attracted interests from researchers and pharmaceutical industry. There has been much effort on designing effective drug repositioning approaches including expression profile analysis, network analysis, pathway-based method and so on.

In our lab, we study on similarity-based method for developing prediction models for repositioning candidates with machine learning algorithms such as linear regression, SVM, and random forest.

[다수준 생체분자특성을 적용한 협력형 MCMT 스마트 지식기반시스템 개발/ Development of a smart MCMC knowledge-base system based on multi-level bio-molecule characteristics, 2014.09.01~2017.08.31, 유전자동의보감사업단, 과학기술정보통신부]


2. Prediction of Drug-target Interaction

Drugs activate and inhibit biological activity of target protein by binding. Thus, identification of drug-target interaction plays important role in drug discovery. However, biological and chemical experiments are laborious so cost a lot. Therefore, identification of drug-target interaction by computational methods is good alternative model to reduce cost with accumulation of drug-target interaction data and advance in data science.








To construct prediction model, first, gather drug-target data from various source (DrugBank, KEGG, IUPHAR). Second, transform the data to vectors, which is understandable for computer. Finally, Build prediction model with machine learning methods (SVM, k-NN, KRM).

[기계학습 방법론과 네트워크 시뮬레이션을 이용한 신규 약물후보 예측 기술 개발/Drug prediction through machine learning techniques and network simulation, 2015.11.01~2018.10.31, 신진연구지원사업, 과학기술정보통신부]

3. Epigenomics

(1) Tracking stem cell differentiation and predicting of cell pluripotency

To understand the molecular mechanisms associated with cellular differentiation, we focus on research on dynamic DNA methylation during cell differentiation and self-renewal. In this research, we perform tracing dynamic changes of DNA methylation at single cell resolution and constructing computational models for predicting cell pluripotency by methylome sequencing data including other omics-data. Here, we approach the computational modeling method for multivariate and large scale numerical matrix analysis such as regression analysis. Finally, we aim to identify molecular biomarkers of cell differentiation for understanding dynamic cell heterogeneity during epigenetic reprograming which can be candidates of regenerative medicines.

(2) Computational modeling for biological age prediction and identifying epigenetic bio-marker aging control

Aging involves various types of biological pathways and finding aging-related biomarkers, such as telomere length, gene expression provide insight into age-associated diseases. In this research, we would like to construct age prediction models using DNA methylation data and to find aging-related epigenetic markers in multiple tissues. For model construction, we tries to overcome high dimensional problems in biological data by applying regularization technique and various machine learning algorithms. Furthermore, we try to make comparative analysis using various omics data including m6A (N6-Methyladenosine) RNA immunoprecipitation approach followed by high-throughput sequencing (MeRIP-seq) between old and young mice. So, our aims of this research are finding aging-related epigenetic markers and understanding the mechanisms of human aging

[생물정보학을 이용한 생명노화 조절인자 및 지표 발굴/Age-related regulators and biomarkers identification via bioinformatics driven approaches, 2016.1.1~2017.12.31, 실버헬스바이오 기술개발사업, 광주과학기술원 실버헬스바이오 연구센터]

Computational Systems Biology Lab.

School of Electrical Engineering and Computer Science (EECS),

Gwangju Institute of Science and Technology (GIST), 123 Cheomdangwagi-ro, Buk-gu, Gwangju, 61005, Republic of Korea