10/08/2026
What if an AI model could learn from both existing biological knowledge and new patterns in data? 🧬
That’s the idea behind engGNN, a model developed by College of Science assistant professor Tiantian Yang and collaborators. It combines known biological relationships with relationships learned from data to help classify diseases and identify features that may be important.
Congratulations to the research team on their publication in Briefings in Bioinformatics!
Read the study: https://doi.org/10.1093/bib/bbag481