ICBO-EAST 2025¶
ICBO-EAST 2025 will be held as a satellite meeting of the 16th International Conference on Biological and Biomedical Ontology (ICBO 2025) in the East Asian time zone. This event is organized in collaboration with the Special Interest Group of Semantic Web Ontology (SIGSWO) of the Japanese Society for Artificial Intelligence (JSAI) and OntoChina, in conjunction with the main ICBO organizing committee.
ICBO-EAST 2025 will be hosted in person in Nara, Japan, at the same venue as The 24th-International Semantic Web Conference (ISWC 2025), allowing participants to benefit from both communities.
The meeting will feature a two-part one day schedule designed to enhance accessibility and regional engagement:
- Morning sessions (in English) will align with the main ICBO conference's Themes, featuring keynote talks, invited presentations, and selected paper sessions open to a global audience.
- Afternoon parallel sessions (in multiple languages) will provide regionally focused programming and discussions in local languages (e.g., Japanese, Chinese, and Thai), encouraging broader participation from East Asian communities.
ICBO-EAST aims to foster deeper collaboration across global and local biomedical ontology communities by bridging time zones, languages, and scientific perspectives.
Date and Time: Saturday, November 1, 2025, 10:00 AM - 13:00 JST, online general session (i.e., 9:00 - 12:00 Beijing Time)
- Japan parallel session on-site at ISWC venue, SIG-SWO (14:00-17:30 JST
- China parallel session on-site at Harbin Medical University (13:00-16:30 Beijing Time)
- Thailand online session, OntoThailand (13:00-15:30 BKK Time)
Format: Online (Hybrid participation may be available for the Japan-based SWO session at the ISWC 2025 venue in Nara)
Registration Fee: Free
Important Dates¶
- Online General Session (10:00 - 13:00 JST, 9:00 - 12:00 Beijing Time, 08:00 - 11:00 Bangkok Time)
- Japan parallel session on-site at ISWC venue, SIG-SWO (14:00-17:30 JST
- China parallel session on-site at Harbin Medical University (13:00-16:30 Beijing Time)
- Thailand parallel session, online, OntoThailand (13:00-15:30 BKK Time)
Call for Papers - ICBO-EAST Track¶
We invite submissions for approximately eight presentations for the ICBO-EAST track (in English, focused on life science topics).
We welcome papers related to ontology development and application in biomedical and life science domains covered by ICBO.
- Paper Submission Deadline: August 24, 2025 September 7, 2025
- Notification of Acceptance: October 15, 2025
- Submission Site: https://cmt3.research.microsoft.com/ICBO2025/Track/2/Submission/ If this link does not work for you, try the following link: https://cmt3.research.microsoft.com/ICBO2025/Submission/Index
Registration¶
Please register via the main ICBO 2025 conference website.
Keynote Speakers¶
Dr. Achille Zappa and Dr. Yukie Akune-Taylor
- Title: Glycoscience data and ontologies for understanding the expanded central dogma
- Bio: Dr. Achille Zappa and Dr. Yukie Akune-Taylor are conducting research at Glycan and Life Systems Integration Center (GaLSIC) at Soka University with Professor Kiyoko Aoki-Kinoshita. Their work focuses on exploring the complex functions of glycans using computational analysis and data mining techniques. This includes the development of glycan semantic knowledgebases, linked-data schemas and ontologies, as well as the analysis of glycan–protein-lipid interactions.
- Abstract: Glycans play key roles for fundamental life-processes such as protein folding, cell–cell communication, immune regulation, and development, yet their template-free biosynthesis and vast structural diversity make them challenging to study systematically. Our laboratory integrates glycoscience data with formal ontologies, semantic web technologies and linked-data principles to elucidate glycan-mediated regulatory mechanisms. We encode glycan structures, biosynthetic pathways, and functions in machine-readable scheme to enable interoperability with genomic, proteomic, and glycomic datasets. This semantic integration supports construction of system-level knowledge graph models that capture biochemical complexity and dynamic glycan modifications. By embedding glycans within functional networks, we extend the classical central dogma to represent multilayered regulation of gene expression and protein activity. The resulting knowledge framework accelerates hypothesis generation and data-driven knowledge discovery, elevating glycoscience within systems biology and advancing mechanistic understanding of molecular regulation in health and disease, and translational applications for diagnostics and therapeutics.
Dr. Jian Du
- Title: Utilizing Large Language Models to Build Causal Diagrams for Observational Health Research
- Abstract: Determination of causality is a fundamental objective in observational health research. Causal diagrams, also known as causal Directed Acyclic Graph (DAG), are increasingly utilized to depict assumed causal relations among exposures, outcomes, and secondary variables such as confounders, colliders, and mediators of the effects of interest. However, constructing DAGs typically relies on human-readable literature or significance tests for variable correlations, which is impractical with the growth of scientific literature and complexity of variable interactions. DAGs typically involve three fundamental types: common causes (confounders), common effects (colliders), and intermediates between the investigated exposure-outcome variables. Manually building such a graph with distinct structural information is time-consuming, error-prone, and unsystematic for health researchers. This lecture introduces a knowledge-driven approach that integrates Large Language Models (LLMs) with knowledge graph to automate the construction of DAGs. It is expected to reduce information burden for health researchers in variables selection during causal modeling using observational health data.
- Bio: Jian Du obtained his PhD degree from School of Information Management, Nanjing University. He is am now an assistant professor at the National Institute of Health Data Science, Peking University. He has a mixed background of Biomedical Informatics and Library & Information Science. Before joining Peking University, he served as a research scientist at Institute of Medical Information, Chinese Academy of Medical Sciences. Since 2020, he has led the Computable Biomedical Knowledge Group (cbk.bjmu.edu.cn), which focuses on natural language processing, literature mining and knowledge graph construction, and their applications in clinical epidemiology for major chronic diseases. His group is currently funded by the National Key R&D Program for Young Scientists and the National Natural Science Foundation of China. Dr. Du also contributes to the academic community as an editorial board member for Scientific Data, Health Data Science and BMC Medical Informatics and Decision Making.
Dr. Junguk Hur
- Title: Unlocking Biomedical Knowledge with LLMs: Literature Mining for Protein Interactions and Vaccine Adjuvant Research
- Abstract: Large language models (LLMs) such as GPT, Llama, and Gemma are transforming biomedical text mining by enabling contextual understanding beyond traditional rule-based systems like SciMiner. This keynote introduces applications of LLMs for extracting protein–protein interactions and vaccine adjuvant information from the scientific literature. GPT-based models achieved performance comparable to BioBERT in identifying PPIs, while LLMs effectively recognized adjuvant names and immune response mechanisms, contributing to knowledge integration within VIOLIN and Vaxjo. Together, these studies demonstrate how LLMs can convert unstructured biomedical literature into structured, actionable knowledge to accelerate discoveries across molecular and vaccine research.
- Bio: Junguk Hur, Ph.D. is an Associate Professor in the Department of Biomedical Sciences and Director of the Computational Data Analysis Core at the University of North Dakota School of Medicine and Health Sciences. His research integrates bioinformatics, systems biology, and literature mining to analyze and interpret multi-omics and biomedical big data, with applications in metabolic diseases, neurodegeneration, and host-pathogen interactions. Dr. Hur also leads ontology-driven research to improve data integration and knowledge discovery across diverse biomedical domains.
Invited Speakers¶
Dr. Bairong Shen
- Title: Disease-Specific Ontology-Driven Intelligent Medicine
- Abstract: Large language models (LLMs) lack clinical domain expertise, limiting their medical use—underscoring the need for disease-specific ontologies (DSOs) to advance precision and intelligent medicine. Unlike broad ontologies (e.g., ICD-11, SNOMED CT), DSOs deliver in-depth, standardized disease representations, fulfilling demands for detailed, condition-specific data. Over the past decade, we developed MIO, EXMO, PCLiON, and PCAO2, which enable deep data sharing in precision care. Their iterative evolution supports granular data collection, foundational for patient digital twins and personalized profiles. Integrated with knowledge graphs (KGs), DSOs enhance intelligent medicine: aiding sepsis care via LLM-driven KGs and RAG platforms (e.g., MetaSepsisKnowHub); enabling prostate cancer tools like RARPKB and ontology-based chatbots; strengthening rare disease research through graph-augmented LLMs; and supporting lung cancer analysis via UMLS-powered patient graphs. DSOs’ potential lies in expanding precision KGs horizontally, offering an alternative to vertical LLM applications and boosting collaborative, personalized care.
- Bio: Dr. Bairong Shen is the Director and Professor of the Institute of System Genetics at West China Hospital of Sichuan University. Currently, he also serves as a visiting professor at the Institute for Systems Biology in Seattle and the University of the Basque Country in Spain. He is the head of the virtual teaching and research laboratory for the "Medical Data Collection and Analysis based on Science, Technology, and Information" course under the "101 Plan" of the Ministry of Education. Since returning to China in 2008, he has led more than 10 national projects and supervised over 100 graduate students. He has published over 300 papers in international interdisciplinary journals and edited more than 10 English and Chinese books in the field of translational informatics. His research interests include the theory of biomarker discovery, biomedical data sharing and security, and intelligent chronic disease management.
Program¶
See program HERE
Organizers¶
- Hiroshi Masuya (RIKEN BioResource Research Center, Ibaraki, Japan
- Yuki Yamagata (RIKEN BioResource Research Center, Ibaraki, Japan
- Xiaolin Yang (Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China)
- Liang Cheng (Harbin Medical University, Harbin, China)
- Anuwat Pengput (Sirindhorn College of Public Health Khon Kaen, Khon Kaen, Thailand)
- Yongqun "Oliver" He (University of Michigan, Michigan, US)
- Asiyah Yu Lin (OntoData Research and Solutions, LLC., Maryland, US)
- Takanori Ugai (Fujitsu, Japan)
- Eiichi Sunagawa (Toshiba, Japan)
- Shusaku Egami (National Institute of Advanced Industrial Science and Technology, Japan)
- Tetsuya Mihara (Tsukuba University, Japan)
- Atsuko Yamaguchi (Tokyo City University, Japan)
Links¶
Contact Information¶
Please direct all further questions to Asiyah Yu Lin (ontology.world@gmail.com)