Onto2Vec: joint vector-based representation of biological entities and their ontology-based annotations

Abstract

We propose the Onto2Vec method, an approach to learn feature vectors for biological entities based on their annotations to biomedical ontologies. Our method can be applied to a wide range of bioinformatics research problems such as similarity-based prediction of interactions between proteins, classification of interaction types using supervised learning, or clustering.

References in corpus (6)

• Efficient Estimation of Word Representations in Vector Space
• DeepWalk: Online Learning of Social Representations
• Semantic Similarity in a Taxonomy: An Information-Based Measure and its Application to Problems of Ambiguity in Natural Language
• A Review of Relational Machine Learning for Knowledge Graphs
• Neural-Symbolic Learning and Reasoning: A Survey and Interpretation
• Neuro-symbolic representation learning on biological knowledge graphs

Cited by in corpus (11)

• BioConceptVec: creating and evaluating literature-based biomedical concept embeddings on a large scale
• Network representation learning systematic review: ancestors and current development state
• Ontology Embedding: A Survey of Methods, Applications and Resources
• Bio-JOIE: Joint Representation Learning of Biological Knowledge Bases
• Predicting Gene-Disease Associations with Knowledge Graph Embeddings over Multiple Ontologies
• Deep learning in bioinformatics: introduction, application, and perspective in big data era
• Embedding Ontologies via Incorporating Extensional and Intensional Knowledge
• OWL2Vec*: Embedding of OWL Ontologies
• Using ontology embeddings for structural inductive bias in gene expression data analysis
• Why Settle for Just One? Extending EL++ Ontology Embeddings with Many-to-Many Relationships
• Link Analysis meets Ontologies: Are Embeddings the Answer?