Graph Neural Network based Hierarchy-Aware Embeddings of Knowledge Graphs: Applications to Yeast Phenotype Prediction
📰 ArXiv cs.AI
Learn to create hierarchy-aware embeddings of knowledge graphs using graph neural networks for improved phenotype prediction in yeast
Action Steps
- Apply graph neural networks to knowledge graphs to learn hierarchy-aware embeddings
- Use semantic loss derived from underlying ontologies to enrich the embeddings
- Configure the model to predict yeast phenotype based on gene deletions
- Test the model on a dataset of yeast Saccharomyces cerevisiae
- Compare the performance of the hierarchy-aware embeddings with traditional embeddings
Who Needs to Know This
Data scientists and AI researchers working on knowledge graph embeddings and bioinformatics applications can benefit from this method to improve prediction accuracy
Key Insight
💡 Hierarchy-aware embeddings of knowledge graphs can improve prediction accuracy in bioinformatics applications
Share This
🧬🤖 Improve yeast phenotype prediction using graph neural networks and hierarchy-aware embeddings! #AI #Bioinformatics
Key Takeaways
Learn to create hierarchy-aware embeddings of knowledge graphs using graph neural networks for improved phenotype prediction in yeast
Full Article
Title: Graph Neural Network based Hierarchy-Aware Embeddings of Knowledge Graphs: Applications to Yeast Phenotype Prediction
Abstract:
arXiv:2605.03690v1 Announce Type: cross Abstract: 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 ontologies. This method yields embeddings that better reflect domain knowledge. To demonstrate their utility, we predict and interpret the effects of gene deletions in the yeast Saccharomyces cerevisiae and learn box embeddings for KGs in the absence of a prediction task. We
Abstract:
arXiv:2605.03690v1 Announce Type: cross Abstract: 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 ontologies. This method yields embeddings that better reflect domain knowledge. To demonstrate their utility, we predict and interpret the effects of gene deletions in the yeast Saccharomyces cerevisiae and learn box embeddings for KGs in the absence of a prediction task. We
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