Ballhysa, E., …, Y. Park, …, & A. Antebi (2026), Loss of killifish cGAS attenuates age-related signatures but does not affect organismal life span. EMBO Reports.

Y. Park and A. Antebi (2026), Deconvolution of Single-Organism Omics Resolves Cellular and Tissue Dynamics during Aging. microPublication Biology. [Code]

J. Kim, H. Jang, Y. Park, I. Jung, & K. Jo (2025), ExPDrug: Integration of an interpretable neural network and knowledge graph for pathway-based drug repurposing. Computers in Biology and Medicine.

Y. Park, AC. Hauschild (2024), The effect of data transformation on low-dimensional integration of single-cell RNA-seq. BMC Bioinformatics. [Code] [Online supplementary]

Y. Park, N. P. Muttray, AC. Hauschild (2024), Species-Agnostic Transfer Learning for Cross-species Transcriptomics Data Integration without Gene Orthology. Briefings in Bioinformatics. [Code1] [Code2]

Y. Park, AC. Hauschild, D. Heider (2021), Transfer Learning Compensates Limited Data, Batch Effects, And Technological Heterogeneity In Single-Cell Sequencing, NAR Genomics and Bioinformatics. [Code]

Y. Park, D. Heider, AC. Hauschild (2021), Integrative Analysis of Next-Generation Sequencing for Next-Generation Cancer Research toward Artificial Intelligence, Cancers. *ISSUE COVER

D. Lee*, Y Park*, S Kim (2020), Towards multi-omics characterization of tumor heterogeneity: a comprehensive review of statistical and machine learning approaches, Briefings in Bioinformatics. 

S. Seo, M. Oh, Y. Park, S. Kim (2018), DeepFam: Deep learning based alignment-free method for protein family modeling and prediction. ISMB 2018. Bioinformatics.

S. Lee, Y. Park, S. Kim (2017), MIDAS: Mining differentially activated subpaths of KEGG pathways from multi-class RNA-seq data. Methods.

Y. Park, S. Lim, J. Nam, S. Kim (2016), Measuring intratumor heterogeneity by network entropy using RNA-seq data, Scientific Reports. [Code]

S. Lim, Y. Park, B. Hur, M. Kim, W. Han, S. Kim (2016), Protein Interaction Network (PIN) -based Breast Cancer Subsystem Identification and Activation Measurement for Prognostic Modeling, Methods.

Moradpour, Maryam, … Y. Park, & AC. Hauschild (2026), FederatedRSF: Federated Random Survival Forests for Partially Overlapping Medical Data. arXiv.

V. E. Martinez-Miguel, …, Y. Park, …, & A. Antebi (2026), Modulation of the RNAse P/MRP complex and mitochondrial ribosome enhances cytosolic ribosome coordination and sustains longevity. bioRxiv.

Y. Park, M. Weig, C. Noll, AC. Hauschild, O. Bader (2024), MS-UMG: MALDI-TOF Mass Spectra and Resistance Information on Antimicrobials from University Medical Center Göttingen (1.0)  [Data set]. Zenodo.

Y. Park, M. Weig, C. Noll, O. Bader, AC. Hauschild (2024), Effect of Data Heterogeneity in Clinical MALDI-TOF Mass Spectra Profile on Direct Antimicrobial Resistance Prediction through Machine Learning. bioRxiv. [Code]

Y. Park, C. E. Schmidt, B. M. Batton, AC. Hauschild (2024), Federated Random Forest for Partially Overlapping Clinical Data. arXiv. [Code1] [Code2]

GMDS2026 - [Abstract] A Federated Artificial Intelligence Framework for Optimizing Pancreatic Cancer Treatment–a Technical Case Report – 70. Jahrestagung der Deutschen Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie e. V. [Abstract]

RECOMB-Microbiome2025 - [Lecture] Multi-Label Classification with Masked Loss for Predicting Multi-Antibiotic Resistance in Mixed-Species MALDI-TOF MS Profile – 29th Annual International Conference on Research in Computational Molecular Biology

ACC25 - [Poster] Prediction of fibrosis in patients with severe aortic stenosis using machine learning models – American College of Cardiology [Abstract]

GCB2024 - [Poster] Addressing Data Imbalance in MALDI-TOF Mass Spectra Dataset for Machine Learning Model for Direct Antimicrobial Resistance Prediction – The German Conference on Bioinformatics

ISMB2024 - [Workshop] Federated Ensemble Learning for Biomedical Data – 2024 International Conference on Intelligent Systems for Molecular  Biology

GMDS2023 - [Lecture] Framework for Federated Artificial Intelligence for the Optimization of Pancreatic Cancer Treatment – 68. Jahrestagung der Deutschen Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie e. V. [Abstract]

GCB2023 - [Lecture] Heterogeneous domain adaptation for cross-species transfer learning without gene homology
[Workshop] Federated Ensemble Learning for Biomedical Data – The German Conference on Bioinformatics

ISMB/ECCB2023 - [Lecture (SCS) & Poster] Heterogeneous Domain Adaptation for Species-Agnostic Transfer Learning – 2023 International Conference on Intelligent Systems for Molecular  Biology & 22nd European Conference on Computational Biology

GLBIO2023 - [Poster] Heterogeneous Domain Adaptation for Species-Agnostic Transfer Learning – 15th Great Lakes Bioinformatics Conference

ECCB2022 - [Poster] Impact of preprocessing in data integration of single-cell RNA-seq data – 21st European Conference on Computational Biology [Poster]

GCB2022 - [Poster] Impact of preprocessing in data integration of single-cell RNA-seq data – The German Conference on Bioinformatics

GMDS2022 - [Lecture] Potential Applications of Transfer Learning in Limited Biomedical Data – 67. Jahrestagung der Deutschen Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie e. V. [Abstract]

ASHG2021 - [Poster] Meta-Learning on Next-Generation Sequencing Data to Improve Cell Type Annotation – American Society of Human Genetics

IEEE-BigComp2017 - [Proceeding] Flow maximization analysis of cell cycle pathway activation status in breast cancer subtypes – IEEE International Conference on Big Data and Smart Computing (BigComp) [BEST PAPER AWARD]

BIOINFO2016 - [Poster] Measuring intratumor heterogeneity by network entropy using RNA-seq data – Annual Conference of Korean Society for Bioinformatics

KOGO2015 - [Workshop] Cytoscape practice with KEGG pathway (in Korean) – The 11th Korea Genome Organization Winter Symposium