Course description
Artificial intelligence (AI) is becoming an increasingly valuable addition to computational biology workflows. As researchers generate larger and more complex omics datasets, AI can help provide biological context, accelerate data interpretation and streamline analytical workflows.
Designed for researchers who are starting to work with omics data and want to incorporate AI into their analysis workflows, this practical one day course explores how AI can be integrated directly into bioinformatics pipelines. Through expert guidance and hands on exercises, you'll learn how to call AI models programmatically from R, build retrieval augmented generation (RAG) pipelines and use AI to interpret omics datasets using trusted biological resources.
Rather than focusing solely on AI concepts, this course demonstrates when and where different AI approaches add value to omics workflows, helping you make informed decisions about how to apply them within your own research.
The course covers:
- Understanding advanced AI concepts, including retrieval augmented generation (RAG), agentic AI, foundation models and biomedical knowledge graphs
- Understanding when each AI approach adds value to an omics workflow
- Choosing between API, local and cloud based AI models based on cost, speed, scale and data privacy
- Querying AI models via an API using the ellmer package in R
- Working with protein language models, including ESMFold
- Building an omics contextualisation RAG pipeline linking PubMed, UniProt and a large language model
- Applying AI to real immunology case studies using Human Cell Atlas data
The course is delivered by computational immunologists with experience across academia, biotechnology and the pharmaceutical industry, who understand both the biological questions researchers are trying to answer and the challenges of integrating new computational approaches into research.
You will gain:
- Practical experience incorporating AI into your existing analysis workflows
- Confidence calling AI models directly from R
- An understanding of when different AI approaches are most appropriate for different research questions
- Experience building AI enabled bioinformatics pipelines
- Practical skills for interpreting complex omics datasets using AI
- Transferable computational skills that can be applied across a wide range of biological research projects
Registration
This course is offered at a discounted rate to all BSI members. You can sign up to become a member online.
| BSI member | £395 + VAT |
| Non member | £775 + VAT |
BSI members also benefit from discounted registration for BSI events, access to journals, grants, career development opportunities and much more.
Who should attend?
This course is designed for researchers who are beginning to work with omics data and want to integrate AI into their analysis workflows.
It is particularly suitable for:
- Computational biologists
- Bioinformaticians
- PhD students and postdoctoral researchers working with omics data
- Researchers analysing transcriptomic, single cell or spatial datasets
- Scientists looking to integrate AI into reproducible analysis workflows
- Researchers interested in applying AI to biological data interpretation
Basic knowledge of R is recommended, but all code is provided as R Markdown and pre-course R materials will be shared to help those newer to coding get the most from the course.
Meet the trainer
Dr Jason Cosgrove
Principal Scientist, Computational Medicine
Jason is a computational immunologist with more than 10 years experience across academia, biotechnology and the pharmaceutical industry. Specialising in computational biology, AI and systems immuno-hematology, he delivers practical, research focused training designed to help biologists confidently apply computational approaches in their research.
Continue your AI learning journey
This course forms part of the BSI's AI and machine learning training programme for researchers.
Whether you're developing your AI skills for the first time or looking to apply machine learning to more advanced biological questions, our programme offers courses to support every stage of your learning journey.
View the full training programme to discover training on AI research assistants and machine learning for immune profiling, prediction and biomarker discovery.
