Sungjoon Park

📕 Bio

Sungjoon Park is a postdoctoral researcher in SNU Bioinformatics Institute. He earned his PhD in Computer Science and Engineering in 2024. With a passion for bridging the fields of bioinformatics and cheminformatics, Sungjoon's research centers around translating and optimizing complex problems from these domains into computational frameworks. His expertise lies in crafting interpretable models and interactive systems that empower domain experts by offering guidance on utilizing cutting-edge methods.

Beyond his academic pursuits, Sungjoon enjoys cycling, sports climbing, and playing video/tabletop games. He values these endeavors as opportunities for relaxation and personal growth, complementing his intellectual endeavors.

🎓 Education

  • Doctor of Philosophy '18 - '24
    Department of Computer Science and Engineering, Seoul National University
    • Thesis: "Empowering domain experts with cloud-based drug prediction tools: from kinase inhibition to drug response and side effects"
    • Advisor: Prof. Sun Kim
  • Bachelor of Science '11 - '17
    Department of Computer Science and Engineering, Seoul National University
    • Major: Computer Science and Engineering

🔭 Research Projects

  • AI-based drug discovery '19 – Present
    • In silico virtual high-throughput screening & absorption, distribution, metabolism, excretion and toxicity (ADMET) prediction with machine learning and AI techniques
    • Linking a large in vivo clinical database of The Cancer Genome Atlas (TCGA) to in vitro experiments from Cancer Cell Line Encyclopedia (CCLE) using matrix factorization on the cloud system to recommend a personalized medicine
    • Prediction of drug side-effect frequency by mapping drugs and side effects onto a common embedding space using deep learning and ensemble methods
  • Multi-omics integrative analysis on the cloud '20 – '22
    • Survey on machine learning methods to investigate gene regulations by utilization of multi-omics data
    • Deploying an integrative analysis pipeline on Amazon Web Service (AWS) by combining tools for single nucleotide variations (SNVs), transcriptomics, copy number variations (CNVs), and DNA methylation
  • Study of COPD pathogenesis using machine learning '20 – '23
    • Etiological study of environmental factors such as cigarette smoke extract and particulate matter in chronic obstructive pulmonary disease (COPD) proteome data from air-liquid interface (ALI) cultured cells
    • Cross validation with independent public data of single cell transcriptomics from Sequence Read Archive (SRA)
  • Homomorphic encryption to detect point mutations '18 – '19
    • Assay genomic sequence to find single nucleotide polymorphisms (SNPs) without sharing a private key of the encrypted patient DNA with the hospital
    • Devise of the first secure SNP panel scheme to encrypt the genomic data using an open source homomorphic encryption library (HEAAN)

🏹 Skills

  • Machine learning Deep learning, traditional machine learning, ensemble methods
  • Cloud system experience Amazon Web Service (AWS), Oracle Cloud Infrastructure (OCI)
  • Server maintenance CentOS, Debian, Ubuntu
  • Web development Nodejs, django
  • Programming languages C/C++, Python, JavaScript, Bash shell, SQL, R, etc.

📚 Publications

  1. in press M Pak, D Jeong, S Park, J Gu, S Lee and S Kim. "ALPACA: A Visual Data Mining System for Subcellular Location-specific Knowledge Mining from Multi-Omics Data in Cancer." Accepted to BMC Bioinformatics .
  2. lead S Park†, S Lee†, M Pak and S Kim. "Dual representation learning for predicting drug-side effect frequency using protein target information." Journal of Biomedical and Health Informatics (2024). doi:10.1109/jbhi.2024.3350083
  3. lead JK Yoon†, S Park†, KH Lee†, D Jeong, J Woo, J Park, SM Yi, D Han, CG Yoo, S Kim and CH Lee. "Machine Learning-Based Proteomics Reveals Ferroptosis in COPD Patient-Derived Airway Epithelial Cells Upon Smoking Exposure." Journal of Korean Medical Science 38.29 (2023). doi:10.3346/jkms.2023.38.e220
  4. preprint Y Lu, S Lim, S Park, MG Choi, C Cho, S Kang and S Kim. "EnsDTI-kinase: Web-server for Predicting Kinase-Inhibitor Interactions with Ensemble Computational Methods and Its Applications." bioRxiv (2023): 2023-01. doi:10.1101/2023.01.06.523052
  5. lead S Park†, D Lee†, Y Kim, S Lim, H Chae and S Kim. "BioVLAB-Cancer-Pharmacogenomics: tumor heterogeneity and pharmacogenomics analysis of multi-omics data from tumor on the cloud." Bioinformatics 38.1 (2022): 275-277. doi:10.1093/bioinformatics/btab478
  6. S Lim, Y Lu, CY Cho, Y Kim, S Park and S Kim. "A review on compound-protein interaction prediction methods: data, format, representation and model." Computational and Structural Biotechnology Journal 19 (2021): 1541-1556. doi:10.1016/j.csbj.2021.03.004
  7. lead M Oh†, S Park†, S Kim and H Chae. "Machine learning-based analysis of multi-omics data on the cloud for investigating gene regulations." Briefings in bioinformatics 22.1 (2021): 66-76. doi:10.1093/bib/bbaa032
  8. M Oh, S Park, S Lee, D Lee, S Lim, D Jeong, K Jo, I Jung and S Kim. "DRIM: a web-based system for investigating drug response at the molecular level by condition-specific multi-omics data integration." Frontiers in Genetics 11 (2020): 564792. doi:10.3389/fgene.2020.564792
  9. lead S Park, M Kim, S Seo, S Hong, K Han, K Lee, JH Cheon and S Kim. "A secure SNP panel scheme using homomorphically encrypted K-mers without SNP calling on the user side." BMC genomics 20 (2019): 163-174. doi:10.1186/s12864-019-5473-z

🖥️ Presentations

  • poster 2023 SNU Artificial Intelligence Institute Retreat "Dual representation learning for predicting drug-side effect frequency using protein target information."
  • poster AI for Drug Discovery Symposium, MOGAM Institute of Biomedical Research "BioVLAB-Cancer-Pharmacogenomics: Tumor heterogeneity and pharmacogenomics analysis of multi-omics data from tumor on the cloud."
  • poster The 6th SNU Bioinformatics Research Exchange Conference "Multi-omics integrative analysis pipelines of cancer pharmacogenomics."
  • oral ICGC ARGO 17th Scientific Workshop / 4th ARGO Meeting "BioVLAB-Cancer-Pharmacogenomics: Tumor heterogeneity and pharmacogenomics analysis of multi-omics data from tumor on the cloud."
  • oral The 17th Asia Pacific Bioinformatics Conference (APBC 2019) "A secure SNP panel scheme using homomorphically encrypted K-mers without SNP calling on the user side."

🏆 Awards & Honors

  • 2022 1H Talented Researcher Fellowship BK21 FOUR Graduate School Innovation Project
  • 2021 Star Student Researcher Award BK21 FOUR Intelligence Computing
  • Standigm/Korean Society for Bioinformatics Best Paper Award BIOINFO 2021, Korean Society for Bioinformatics
  • Second-tier Travel Fellowship The 17th Asia Pacific Bioinformatics Conference

🌏 Languages

  • 🇰🇷 Korean (native)
  • 🇺🇸 English (fluent)
  • 🇯🇵 Japanese (intermediate)