Using AI/ML for Space Biology Research

As the field of space biology continues to generate vast amounts of data, the ability to effectively analyze and interpret this information is becoming increasingly essential. To meet this growing need, the “Artificial Intelligence/Machine Learning (AI/ML) in Space Biology Training” course has been developed to provide researchers with the skills needed to apply artificial intelligence and machine learning techniques to complex biological data. Designed as a self-paced, comprehensive online program, the course combines foundational knowledge in space biology with hands-on experience in data processing, machine learning model development, and bioinformatics.

Through the NASA TOPS-T ScienceCore grant awarded to the staff of OSDR and Space Biology, “AI/ML in Space Biology Training” is a comprehensive course designed to equip researchers with foundational skills in data analysis and machine learning tailored specifically for space biology. The course includes pre-recorded lectures, Python notebooks, and quizzes, and is offered as a self-paced online curriculum. In Module 1, participants will be introduced to the fundamental concepts of space biology, gain familiarity with Google Colab, and explore the basics of machine learning and space biology data. Module 2 focuses on practical data processing techniques, including working with tabular and image data, and visualizing datasets to uncover insights.  Module 3 delves into building essential machine learning models, covering clustering, regression, and classification techniques. Participants will learn how to apply these models to analyze and interpret complex biological data. Finally, Module 4 emphasizes the importance of result interpretation through bioinformatic tools, explainable AI, and the principles of open science, ensuring that findings are transparent and accessible.

By the end of this course, learners will have a robust understanding of how to leverage AI and ML methodologies to advance space biology research, enhancing their ability to tackle complex biological questions in space environments.  Students can now register for this self-paced class.

	Stay connected with the NASA Open Science Data Repository: