Seoul National University · Biochemistry, Genetics and Molecular Biology
Professor Sun Kim's research lab specializes in computational and systems biology, focusing on the regulatory roles of non-coding RNAs—particularly microRNAs—and their impact on gene expression networks in development and disease. The lab develops advanced bioinformatics tools, such as MMIA and DeepFam, to integrate multi-omics data and predict molecular interactions, including compound-protein interactions and transcription factor networks. A key focus is understanding molecular mechanisms underlying complex phenotypes, such as drug response in cancer and drought resistance in transgenic crops. The lab combines machine learning, systems biology, and high-throughput sequencing data to uncover regulatory pathways and support translational research.
Figures are computed from collected data and may differ slightly.
MicroRNAs (miRNAs) are small (19-24 nt), nonprotein-coding nucleic acids that regulate specific 'target' gene products via hybridization to mRNA transcripts, resulting in translational blockade or transcript degradation. Although miRNAs have been implicated in numerous developmental and adult diseases, their specific impact on biological pathways and cellular phenotypes, in addition to miRNA gene promoter regulation, remain largely unknown. To improve and facilitate research of miRNA functions a
Codes are available at https://bhi-kimlab.github.io/DeepFam.
There has recently been a rapid progress in computational methods for determining protein targets of small molecule drugs, which will be termed as compound protein interaction (CPI). In this review, we comprehensively review topics related to computational prediction of CPI. Data for CPI has been accumulated and curated significantly both in quantity and quality. Computational methods have become powerful ever to analyze such complex the data. Thus, recent successes in the improved quality of CP
HER2 has been found to be amplified in 10-20% of gastric cancers, and is correlated with poor outcome. The aims of this study were to recognize HER2 amplification in gastric cancer cell lines via fluorescence in situ hybridization and to evaluate the growth inhibitory effect of trastuzumab in HER2-amplified cell lines. To elucidate the mechanism of the growth inhibition, we performed cell cycle analysis and immunoblotting of downstream molecules. We also conducted drug interaction studies of tra
This study was designed to investigate at the molecular level how a transgenic version of rice "Nipponbare" obtained a drought-resistant phenotype. Using multi-omics sequencing data, we compared wild-type rice (WT) and a transgenic version (<i>erf71</i>) that had obtained a drought-resistant phenotype by overexpressing <i>OsERF71</i>, a member of the AP2/ERF transcription factor (TF) family. A comprehensive bioinformatics analysis pipeline, including TF networks and a cascade tree, was developed
Supplementary data are available at Bioinformatics online.
An elevated At/Et ratio in the fetal pulmonary artery is independently associated with the development of RDS in preterm infants. These data suggest that fetal pulmonary artery Doppler velocimetry may provide a reliable noninvasive technique to evaluate fetal lung maturity, similar to the way in which middle cerebral artery Doppler has replaced amniocentesis for the assessment of fetal anemia.
http://epigenomics.snu.ac.kr/biovlab_mmia_ngs/.
We suggest that our system can logically guide to select seed genes without additional prior knowledge that makes us free from the seed selection of network propagation issues. We showed that Venn-diaNet can reproduce the research findings reported in the original papers that have experiments that compare two, three and eight experiments. Venn-diaNet is freely available at: http://biohealth.snu.ac.kr/software/venndianet.
The multi-omics molecular characterization of cancer opened a new horizon for our understanding of cancer biology and therapeutic strategies. However, a tumor biopsy comprises diverse types of cells limited not only to cancerous cells but also to tumor microenvironmental cells and adjacent normal cells. This heterogeneity is a major confounding factor that hampers a robust and reproducible bioinformatic analysis for biomarker identification using multi-omics profiles. Besides, the heterogeneity
In this study, we systematically compared and evaluated 13 different pathway activity inference tools based on 5 comparison criteria using pan-cancer data set. This study has two major contributions. First, our study provides a comprehensive survey on computational techniques used by existing pathway activity inference tools. The tools use different strategies and assume different requirements on data: input transformation, use of labels, necessity of cohort-level input data, use of gene relatio
Supplementary data are available at Bioinformatics online.
Our study on DNA methylation modification genes in mutated vs. non-mutated groups could provide useful insight into the epigenetic regulation of DEGs in cancer.
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