Machine Learning and AI in Bioinformatics

Course Description

This course offers a hands-on introduction to machine learning (ML) and artificial intelligence (AI) in bioinformatics. Designed for those with a working knowledge of Python (or similar languages), it focuses on the practical application of existing ML/AI tools to solve real-world biological data challenges.

You will explore how AI and ML are applied across a range of bioinformatics tasks and investigate the factors that influence their performance-such as data quality, integration, model explainability, ethical considerations, regulatory issues, and infrastructure. By the end of the course, you'll have the foundational skills to apply AI methods to biological data and evaluate their effectiveness in research and clinical contexts 

Learning Outcomes

At the conclusion of the course, you should be able to

• Explain the role and applications of artificial intelligence and machine learning in bioinformatics research and analysis.
• Evaluate data quality and the impact of data quality, integration, and preprocessing on the performance of AI models in bioinformatics.
• Analyze model explainability and ethical considerations.
• Demonstrate proficiency in using machine learning and AI tools for bioinformatics analysis by applying them to biological data and making informed predictions.
• Discuss the computational and technological infrastructure required to implement AI solutions in bioinformatics effectively.
• Evaluate practical AI case studies.

Topics Include

• Practical AI applications through curated case studies
• Loading and preprocessing data for AI analysis
• Using AI to generate code templates for bioinformatics tasks
• Building models with tools such as Graph TD or Microsoft Graph
• Exploring use cases with Gemini Deep Research and/or OpenAI tools

Notes

Prerequisite Skills: Working knowledge of Python or similar programming language

Additional Information

AI* - This course encourages students to use AI tools such as ChatGPT and Claude to assist with coding, data analysis, and model development, while maintaining the ability to clearly explain, validate, and justify all AI-generated code, methodologies, and results and demonstrating a strong understanding of the underlying biological and computational concepts.