# Predicting Gene-Disease Associations with Knowledge Graph Embeddings over Multiple Ontologies

arXiv: https://arxiv.org/abs/2105.04944 · HTML: https://mlsanity.com/paper/2105.04944/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 29 references that are in the map.
- First posted on arXiv 11 May 2021.

## Builds on (most cited first, 6 of 6)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Embedding Entities and Relations for Learning and Inference in Knowledge Bases](https://mlsanity.com/paper/1412.6575/) (2014)
- [OWL2Vec*: Embedding of OWL Ontologies](https://mlsanity.com/paper/2009.14654/) (2020)
- [Neuro-symbolic representation learning on biological knowledge graphs](https://mlsanity.com/paper/1612.04256/) (2016)
- [OPA2Vec: combining formal and informal content of biomedical ontologies to improve similarity-based prediction](https://mlsanity.com/paper/1804.10922/) (2018)
- [Onto2Vec: joint vector-based representation of biological entities and their ontology-based annotations](https://mlsanity.com/paper/1802.00864/) (2018)

## Built on by (most cited first, 6 of 6)

- [Refining Diagnosis Paths for Medical Diagnosis based on an Augmented Knowledge Graph](https://mlsanity.com/paper/2204.13329/) (2022)
- [Benchmark datasets for biomedical knowledge graphs with negative statements](https://mlsanity.com/paper/2307.11719/) (2023)
- [Link Analysis meets Ontologies: Are Embeddings the Answer?](https://mlsanity.com/paper/2111.11710/) (2021)
- [Knowledge-Driven Mechanistic Enrichment of the Preeclampsia Ignorome](https://mlsanity.com/paper/2207.14294/) (2022)
- [Knowledge Graph Completion based on Tensor Decomposition for Disease Gene Prediction](https://mlsanity.com/paper/2302.09335/) (2023)
- [Biomedical Knowledge Graph Embeddings with Negative Statements](https://mlsanity.com/paper/2308.03447/) (2023)

## Abstract (arXiv)

Ontology-based approaches for predicting gene-disease associations include the more classical semantic similarity methods and more recently knowledge graph embeddings. While semantic similarity is typically restricted to hierarchical relations within the ontology, knowledge graph embeddings consider their full breadth. However, embeddings are produced over a single graph and complex tasks such as gene-disease association may require additional ontologies. We investigate the impact of employing richer semantic representations that are based on more than one ontology, able to represent both genes and diseases and consider multiple kinds of relations within the ontologies. Our experiments demonstrate the value of employing knowledge graph embeddings based on random-walks and highlight the need for a closer integration of different ontologies.