On behalf of the Institute of Biostatistics and Network Science, we are delighted to invite you to the 1st Biomedical Data Science Conference (BIOMED-DATA 25), taking place at Semmelweis University in Budapest, Hungary, from August 6 to 8, 2025. We welcome researchers to join us as presenters or participants, engaging in discussions on cutting-edge topics in biomedical data science.

CALL FOR ABSTRACTS
The conference welcomes papers addressing data-intensive biological and medical research challenges, with a focus on developing novel methodologies to advance biomedical science discovery, including but not limited to:

Machine learning and AI applications in biomedical research

Multi-omics data integration and analysis

Medical imaging and computer vision in healthcare

Natural language processing for clinical and biomedical texts

Explainable AI and trustworthy machine learning in medicine

Computational modeling of biological systems

Wearable and sensor-based health data analytics

Privacy-preserving techniques for biomedical data

Digital health and personalized medicine

Drug discovery and biomarker identification through data science

Network science tools in biomedical research

Causal inference in biomedical research

Biostatistical approaches for biomedical data analysis


The conference also offers valuable networking opportunities, including a Welcome Reception with an Exhibition Talk on Wednesday and, of course, the Conference Dinner on Thursday.

Note that the Conference Abstract Book will be published with an ISBN number.

AIMS AND GOALS OF THE SUMMER SCHOOL
How can networks help us identify critical relationships in biomedical systems? How can we uncover hidden connections between genes and predict disease pathways? How can visualizing medical data improve diagnostics and treatment outcomes? How can machine learning and deep learning models transform the analysis of complex medical data?


If you're eager to explore these questions, join us at the Biomedical Data Science Summer School! This program equips participants with the theoretical foundation and practical skills to apply network science, data visualization, and machine learning to real-world biomedical challenges.


The curriculum focuses on four key areas:

Biomedical Network Science
Learn how networks can model relationships between genes, proteins, diseases, and patient data to uncover patterns and predict interactions.

Medical Data Visualization
Explore practical methods for creating clear, interactive visualizations of complex medical datasets, improving interpretability and decision-making.

Machine Learning on Tabular Medical Data
Apply machine learning techniques to structured datasets, such as electronic health records, to predict outcomes and identify risk factors.

Deep Learning on Unstructured Medical Data
Gain hands-on experience with deep learning models to analyze unstructured data, including medical images, text, and genomic sequences.


GROUP PROJECTS
Participants will work in teams on data-driven projects, applying what they learn in the courses. Mentors will provide guidance, and teams will present their results to an expert jury at the end of the program. Dedicated time is set aside for project work and presentations, ensuring a practical and engaging learning experience.

CREDITS INFO
Participants who successfully complete the summer school will be awarded 4 ECTS. A certificate of completion will be provided by Semmelweis University, which can facilitate credit transfer to your home institution.