These pages contain links to various tools and resources created by the staff and students associated with the two Biomedical Artificial Intelligence Centres for Doctoral Training funded by UKRI at the University of Edinburgh.

Courses and Training

• Introduction to Python for Biomedical Innovation. A student developed crash course in using Python for biomedical data analysis. This course is presented in modules including coding notebooks to allow you to develop practical skills.
• An Introduction to Multi Modal Analysis Using Networks. This workshop was run at the Intelligent Systems for Molecular Biology (ISMB) confrence in Montreal in July 2024. It is a Python based hands on tutorial introducing network methods including network similarity fusion (SNF) and the use of Graph Neural Networks (GNNs) to perofrm classification tasks on patient specific networks (PSNs).
• How to Create a Protein. A short course on proteins and protein design aimed at early career students.

Public Outreach Activities

• Zoo Reviews. A workshop using natural language processing to identify animals from text, group them by species and assess people’s feelings about the animals.
• Spacebound Minds. A workshop with online resources to learn more about mental health and well-being.

Resources from Research Projects

• GNN-Suite - GNN-Suit is a robust modular framework for constructing and benchmarking Graph Neural Network (GNN) architectures in computational biology. GNN-Suite standardises experimentation and reproducibility using the Nextflow workflow to evaluate GNN performance (Kamp et al., 2025)
• MOG-Dx - Multi-omic Graph Diagnosis (MOGDx) is a tool for the integration of omic data and classification of heterogeneous diseases. MOGDx exploits a Patient Similarity Network (PSN) framework to integrate omic data using Similarity Network Fusion (SNF) (Ryan et al., 2024)
• PDBench - PDBench is a dataset and software package for evaluating fixed-backbone sequence design algorithms. The structures included in PDBench have been chosen to account for the diversity and quality of observed protein structures, giving a more holistic view of performance (Castorina et al., 2023)

  References

1. Kamp, S., Stracquadanio, G., & Simpson, T. I. (2025). GNN-Suite: a Graph Neural Network Benchmarking Framework for Biomedical Informatics. ArXiv Preprint ArXiv:2505.10711. https://doi.org/10.48550/arXiv.2505.10711
2. Ryan, B., Marioni, R. E., & Simpson, T. I. (2024). Multi-Omic Graph Diagnosis (MOGDx): a data integration tool to perform classification tasks for heterogeneous diseases. Bioinformatics, 40(9), btae523. https://doi.org/10.1093/bioinformatics/btae523
3. Castorina, L. V., Petrenas, R., Subr, K., & Wood, C. W. (2023). PDBench: Evaluating Computational Methods for Protein-Sequence Design. Bioinformatics. https://doi.org/10.1093/bioinformatics/btad027

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