This two-day workshop focuses on the applications of large language models (LLMs) in bioinformatics. Participants will first be introduced to foundational machine learning (ML) concepts, followed by an in-depth exploration of LLMs and their growing role in bioinformatics. The proposed program will include lectures and hands-on labs covering LLM applications in genomics, proteomics, and natural language processing (NLP) Participants will learn how to use LLMs and which LLMs to use for coding in bioinformatics, gene prediction/annotation, protein structure/function analysis, and large-scale literature analysis in their research.
By the end of the workshop, participants will be able to:
- Explain the key concepts of large language models and their applications in bioinformatics, including genomics, proteomics, and general informatics.
- Apply LLMs to perform bioinformatics coding tasks.
- Utilize LLM-based tools for analyzing bioinformatics data in genomics, including gene prediction/annotation, protein structure/function prediction, and large-scale literature analysis.
- Integrate LLMs into bioinformatics workflows for research and analysis
Post-doctoral fellows, graduate students, bioinformaticians, computational biologists, and researchers in the life sciences domain; Focused on individuals involved in bioinformatics projects, with an interest in applying large language models and artificial intelligence to genomics, proteomics, and NLP research.
Participants should have a basic understanding of bioinformatics and molecular biology. Familiarity with Python programming and command-line tools is required. Some prior exposure to LLMs concepts and tools would be beneficial, though not mandatory.
Introduction to Machine Learning
• Overview of machine learning concepts and applications in bioinformatics.
• Introduction to key algorithms used in machine learning for bioinformatics, including neural networks, decision trees, graphical neural networks and transformers
• Overview of key architectures of transformers (tokenization, embedding, encoder, decoder)
Introduction to Large Language Models (LLMs)
• Basics of LLMs: what they are and how they differ from traditional machine learning models.
• Evolution of LLMs: BERT, GPT, and their relevance to bioinformatics.
• Fine tuning and prompt engineering for improving LLM performance.
LLM Applications in Genomics (Lecture)
• Overview of LLMs applications in genomics.
LLM Applications in Genomics (Lab)
• Hands-on: Running LLMs for genomic analysis, including gene prediction, gene annotation and variant identification
• Hands-on: Fine-tuning an LLMs for genomic analysis, including gene prediction, gene annotation and variant identification.
LLM Applications in Proteomics (Lecture)
• Lecture: How LLMs are used in proteomics for tasks like protein structure prediction and protein functional annotation.
LLM Applications in Proteomics (Lab)
• Hands-on: Using LLMs for structure prediction and functional analysis of proteins.
Natural Language Processing & Information Extraction
• Introduction to using LLMs to extract biological knowledge from scientific literature at scale, covering core architectures like RAG, vector databases, ontologies, and knowledge graphs
• Hands-on: Practical examples of using LLMs to extract knowledge from scientific literature at-scale
AI-Assisted Coding for Bioinformatics
• Lecture: Introduction to AI-agents and their architecture
• Applications of AI-agents in bioinformatics workflows, including tools like GitHub Copilot.
• Hands-on: Practical examples of using AI agents for bioinformatics tasks.
Future Directions and Wrap-Up
• Discussion on the future of LLMs in bioinformatics.
• Wrap-up discussion and Q&A session.
Duration: 2 days
Start: Nov 14, 2026
End: Nov 15, 2026
Status: Application Open
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Canadian Bioinformatics Workshops promotes open access. Past workshop content is available under a Creative Commons License.
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