Primer on LLMs for Biology

Overview

Discover how large language models can be applied to biological research in this comprehensive primer by James Zou at CGSI 2024.

Syllabus

• Introduction to Large Language Models in Biology
Overview of LLMs and their capabilities
Historical context and evolution of LLMs
• Fundamental Concepts of LLMs
Architecture and training of large language models
Key components: tokens, embeddings, layers, and transformers
• Applications of LLMs in Biological Research
Text mining and information extraction in biology
Genomics and proteomics data analysis
Drug discovery and chemical informatics
• Case Studies and Current Research
Examples of LLM applications in recent biological studies
Discussion of groundbreaking projects utilizing LLMs
• Techniques for Fine-Tuning LLMs for Biological Data
Preprocessing biological data for LLM ingestion
Transfer learning and domain adaptation for biology
• Evaluation of LLM Performance in Biological Tasks
Metrics for assessing LLM output quality
Limitations and challenges in biological contexts
• Developing Custom Biological LLMs
Tools and frameworks for building biology-focused LLMs
Best practices for training and deployment
• Ethical and Societal Implications
Considerations for data privacy and security
Encouraging responsible use of LLMs in biology
• Hands-On Workshop
Interactive session for model tuning and experimentation
Guided exercises in LLM application to biological problems
• Conclusion and Future Directions
Emerging trends and innovations in LLMs for biology
Opportunities for further research and development

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Subjects

Data Science