Graph Neural Network based Hierarchy-Aware Embeddings of Knowledge Graphs: Applications to Yeast Phenotype Prediction
arXiv:2605.03690v2 Announce Type: replace 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 further show how box embeddings can serve as the basis for evaluating KG revisions. Our yeast KG i
The continuous advancements in graph neural networks are enabling more sophisticated analyses of complex biological data, pushing the boundaries of AI applications in life sciences.
This development represents a significant step towards more accurate and interpretable AI models for biological systems, potentially accelerating drug discovery and synthetic biology applications.
The ability to generate hierarchy-aware embeddings of knowledge graphs with semantic loss improves the fidelity and interpretability of AI models in complex biological domains like genomics and phenotype prediction.
- · Biotech companies
- · Pharmaceutical R&D
- · AI algorithm developers
- · Synthetic biology researchers
- · Traditional drug discovery methods
Improved prediction of gene functions and drug interactions will become more common.
Computational biology platforms will integrate these advanced GNN methods, leading to faster prototyping and design in synthetic biology.
The enhanced predictive power could dramatically reduce the time and cost for developing new biological products and therapies, fostering new bio-facturing industries.
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Read at arXiv cs.LG