Large Language Models (LLMs) like ChatGPT have exhibited remarkable capabilities in understanding and generating language across diverse disciplines. In the realm of biomedical data science and computational biology, LLMs can significantly aid the processes of information accessibility, data analysis, and knowledge discovery. In this tutorial, we offer an introductory level hands-on guide to understanding and utilizing these LLMs in the field of biomedical data science. Our tutorial begins with leveling the learning ground by providing introductions to LLMs and Biomedical Data Science. Subsequently, we delve into the core applications of LLMs in biomedical data science/computational biology via retrieval-augmented generation, database functionalities, and code generation. To facilitate thought-provoking discussions, pertinent case studies will be discussed, emphasizing how to harness the power of LLMs to bridge the gap between technical feasibility and practical utility in biomedical data science. Furthermore, hands-on exercises are included to enable participants to apply their learning in real-time. Participants will also get acquainted with OpenAI's ChatGPT and open-source LLMs, as well as their design, use cases, limitations, and prospects.

View on ISMB 2024: https://www.iscb.org/ismb2024/programme-schedule/tutorials#vt1

Tutorial registration: https://www.iscb.org/ismb2024/register#tutorials