THE GENBIO COURSE 

COURSE DESCRIPTION

A primary aim of this course is to equip students with the knowledge and skills to leverage generative AI technologies in biomedical research and applications. The course provides a foundation in both biological data understanding and generative AI fundamentals, followed by practical implementation through model training, evaluation, and collaborative project development. Students from diverse backgrounds (computational and biological sciences) will learn to identify, frame, and address biomedical problems using generative approaches.

COURSE AIM

To quickly bring together and cultivate an interdisciplinary class/cohort/community of biology and computer science students who can identify and solve biological problems using GenAI.

COURSE OBJECTIVES

1. The course begins by developing a comprehensive understanding of biological data types, sources, and structures, establishing the foundation for data-driven approaches.

2. This is followed by an introduction to generative AI principles, neural network architectures, and their transformative potential in biomedical applications.

3. Students then learn to identify suitable biomedical problems for generative approaches and acquire practical skills in biological data acquisition and model implementation.

4. A collaborative mini-hackathon provides hands-on experience in rapid prototyping and interdisciplinary teamwork.

5. The course concludes with critical evaluation of ethical considerations and a final project showcase demonstrating applied generative biology solutions.

Midway through the course, we organized a 24-hour hackathon where teams of two applied AI to tackle biomedical challenges. Participants developed innovative prototypes within the tight timeframe, showcasing creativity and technical skill. The event concluded with prizes for the winning and runner-up teams, highlighting the impressive range of solutions generated