Biomedical Data Science Summer School and Conference 2025 (Application deadline extended!)

About

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.

Course leader

Katy Börner; Kosmas Kepesidis; Gergely Palla; Andras Gezsi

Target group

MSc and PhD student, postdoc or interested parties

Course aim

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.

Fee info