Prerequisites
Basic Python knowledge is required. Participants need a laptop.

Summary
In this course, you will learn how to develop scientific software in a way that enables reproducible research and sustainable software. Sustainable scientific software leaves room for further, subsequent implementations and method development, and saves time and effort in the long run; additionally, a sustainable approach follows good scientific practice through making results reproducible. This course is aimed at researchers, doctoral and master students who develop scientific software to carry out their research.

Learning Objectives
After the course participants should be able to
• Use the git version-control system and collaborate on GitHub
• Use an integrated development environment (IDE)
• Use a linter and code formatter
• Understand the best practices for planning a piece of software
• Write Pytest unit tests
• Use Sphinx to build a documentation
• Ensure reproducibility through automated testing with GitHub actions
• Understand the basics of publishing a Python package

14:00 Jawad Alaa Edeen (Supervisor D. Schapiro)
„Leveraging Spatial Omics Technologies to Elucidate Rare Disease Progression and Tissue- Specific Pathobiology“

14:20 Aroj Hada (Supervisor D. Schapiro)
“Advancing Computational Methods for Spatial Omics: Cell Phenotyping and Spatial Niche Mapping”

14:40 Giuseppe Viterbo (Supervisor T. Buck)
“Simulation Based Inference for Galactic Archaeology”

15:00 Feroz Karim (Supervisor R. Wade)
“Explainable Predictions of Histone-Protein Interactions”

15:20 Jona Novljan (Supervisors M. Modic/A. Sasse)
“Uncovering Post-Transcriptional Regulation Through Signal Deconvolution of Global iCLIP Data“

Machine Learning is transforming science, especially the way we do research in medicine. It can analyze non-linear dependencies of structured clinical data, and it is starting to support in the huge amount of existing text and other unstructured information to extract useful information using recent techniques based on large language models. There is also an increasing amount of specific omics data for each patient, which makes it hard to manually inspect all the details. This is where multimodal data analysis comes in, which is the focus of this year's AI in Medicine workshop. Researchers from Sorbonne and Heidelberg will give keynote speeches to provide insight into their research field, which will fuel discussions.