3 papers
cs.LG2026
Graph Neural Network based Hierarchy-Aware Embeddings of Knowledge Graphs: Applications to Yeast Phenotype Prediction
Filip Kronström, Alexander H. Gower, Daniel Brunnsåker +2
We present a method for finding hierarchy-aware embeddings of knowledge graphs (KGs) using graph neural networks (GNNs) enriched with a semantic loss derived from underlying ontolo…
cs.LG2026
The Use of AI-Robotic Systems for Scientific Discovery
Alexander H. Gower, Konstantin Korovin, Daniel Brunnsåker +6
The process of developing theories and models and testing them with experiments is fundamental to the scientific method. Automating the entire scientific method then requires not o…
cs.AI2024
Genesis: Towards the Automation of Systems Biology Research
Ievgeniia A. Tiukova, Daniel Brunnsåker, Erik Y. Bjurström +8
The cutting edge of applying AI to science is the closed-loop automation of scientific research: robot scientists. We have previously developed two robot scientists: `Adam' (for ye…