Seongyong Park

Seongyong Park

Postdoctoral Fellow at CDSL, NCI/CCR, NIH. Computational cell–cell communication, cytokine activity inference, and clinical biomarker discovery.

Publications

Google Scholar is the most current list; this page mirrors it and groups papers by theme. Bold indicates first or co-first author.

Themes: 🫂 cell–cell communication · 🧬 biomarker discovery · 🧩 sparse representation & overlap statistics · 📈 biosignal processing

2025

  1. Shin JM, Park S, Shin K, Seo WY, Kim HS, Kim DK, Moon B, Cha SG, et al. “Temporal convolutional neural network-based feature extraction and asynchronous channel information fusion method for heart abnormality detection in phonocardiograms.” Computer Methods and Programs in Biomedicine 269, 108871 (2025). [code] 📈

  2. Park S, Kim S, Jiang P. “IC2Bert: masked gene expression pretraining and supervised fine tuning for robust immune checkpoint blockade (ICB) response prediction.” Scientific Reports 15, 28044 (2025). [code] 🧬

  3. Chang TG, Park S, Schäffer AA, Jiang P, Ruppin E. “Hallmarks of artificial intelligence contributions to precision oncology.” Nature Cancer 6(3), 417–431 (2025). 🧬

2024

  1. Lee SW, Park S, Kim JY, Moon B, Lee D, Jang J, Seo W, Kim HS, Kim SH, et al. “Impact of preanesthetic blood pressure deviations on 30-day postoperative mortality in non-cardiac surgery patients.” Journal of Korean Medical Science 39(35) (2024). 📈

2023

  1. Park S, Wahab A, Usman M, Naseem I, Khan S. “Artificial intelligence in bioimaging and signal processing.” Frontiers in Physiology 14, 1267632 (2023). 📈

  2. Park S, Ibrahim MS, Wahab A, Khan S. “GMDM: A generalized multi-dimensional distribution overlap metric for data and model quality evaluation.” Digital Signal Processing 134, 103930 (2023). 🧩

  3. Fazal E, Ibrahim MS, Park S, Naseem I, Wahab A. “Anticancer peptides classification using kernel sparse representation classifier.” IEEE Access 11, 17626–17637 (2023). 🧩

  4. Park S, Wahab A, Kim M, Khan S. “Self-supervised learning for inter-laboratory variation minimization in surface-enhanced Raman scattering spectroscopy.” Analyst 148(7), 1473–1482 (2023). 📈

2022

  1. Park S, Yi G. “Development of gene expression-based random forest model for predicting neoadjuvant chemotherapy response in triple-negative breast cancer.” Cancers 14(4), 881 (2022). 🧬

  2. Usman M, Khan S, Park S, Wahab A. “AFP-SRC: identification of antifreeze proteins using sparse representation classifier.” Neural Computing and Applications 34(3), 2275–2285 (2022). 🧩

  3. Park S, Lee J, Khan S, Wahab A, Kim M. “Machine learning-based heavy metal ion detection using surface-enhanced Raman spectroscopy.” Sensors 22(2), 596 (2022). 📈

2021

  1. Park S, Lee J, Khan S, Wahab A, Kim M. “SERSNet: Surface-enhanced Raman spectroscopy based biomolecule detection using deep neural network.” Biosensors 11(12), 490 (2021). 📈

  2. Usman M, Khan S, Park S, Lee JA. “AoP-LSE: antioxidant proteins classification using deep latent space encoding of sequence features.” Current Issues in Molecular Biology 43(3), 1489–1501 (2021). 🧩

2020

  1. Park S, Khan S, Moinuddin M, Al-Saggaf UM. “GSSMD: A new standardized effect size measure to improve robustness and interpretability in biological applications.” IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1096–1099 (2020). 🧩

  2. Park S, Khan S, Wahab A. “E3-targetPred: Prediction of E3-target proteins using deep latent space encoding.” arXiv:2007.12073 (2020). 🧩

  3. Park S, Khan S. “GSSMD: New metric for robust and interpretable assay quality assessment and hit selection.” arXiv:2001.06384 (2020). 🧩

2017

  1. Lee YH, Choi H, Park S, Lee B, Yi GS. “Drug repositioning for enzyme modulator based on human metabolite-likeness.” BMC Bioinformatics 18(Suppl 7), 226 (2017). 🧬

2011–2012 — experimental microfluidics

  1. Park S, Hong X, Choi WS, Kim T. “Microfabricated ratchet structure integrated concentrator arrays for synthetic bacterial cell-to-cell communication assays.” Lab on a Chip 12(20), 3914–3922 (2012). 🫂

  2. Vinuselvi P, Park S, Kim M, Park JM, Kim T, Lee SK. “Microfluidic technologies for synthetic biology.” International Journal of Molecular Sciences 12(6), 3576–3593 (2011). 🫂

  3. Choi WS, Ha D, Park S, Kim T. “Synthetic multicellular cell-to-cell communication in inkjet printed bacterial cell systems.” Biomaterials 32(10), 2500–2507 (2011). 🫂

  4. Park S, Kim D, Mitchell RJ, Kim T. “A microfluidic concentrator array for quantitative predation assays of predatory microbes.” Lab on a Chip 11(17), 2916–2923 (2011). 🫂

Patents

  • Park S, et al. “Method, computer program, and system for analyzing heart valve abnormalities based on heart murmur.” KR Patent App. 10-2023-0014972 (registered 2025-12-30). 📈
  • Park S, et al. “Pulmonary function test apparatus and method using lung sound.” KR Patent App. 10-2023-0022834 (registered 2023-02-21). 📈
  • Park S, et al. “Method for measuring preload of patients undergoing general anesthesia surgery based on acoustic variability index, and electronic device for executing thereof.” KR Patent App. 10-2023-0021404 (registered 2023-02-17). 📈
  • Min B, Yi G, Park S. “System and method for disease prediction based on group marker consisting of genes having similar function.” KR Patent 10-2236194 (issued 2021-03-30). 🧬
  • Kim T, Park S. “Microfluidic concentrator for communication assays of microbes.” KR Patent 10-1330473 (issued 2013-02-04). 🫂
  • Kim T, Park S. “Microfluidic concentrator array for observing predation behavior of microbes.” KR Patent 10-1238556 (issued 2013-02-22). 🫂

Software

Author.

  • secactpy — Python package for secreted-protein activity inference from bulk and single-cell gene expression (hosted under the Jiang Lab data2intelligence organization). 🫂
  • spatial-gpu — GPU-accelerated spatial kernels for neighborhood activity inference on spatial transcriptomics (Python). 🫂

Main contributor (project initiated by B. Ru).

  • ridger — R package for ridge-regression-based activity inference. 🫂

Contributor (Jiang Lab, NCI; maintainer B. Ru).

  • secact — R package for secreted-protein activity inference.
  • spacet — Spatial Cellular Estimator for Tumors.

Additional software and datasets are ongoing at NCI/CDSL.