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Onto2Vec: joint vector-based representation of biological entities and their ontology-based annotations
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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.
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Cited by 1 Pith paper
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Extending Ontologies: From Dense Embeddings to Hybrid Quantum-Fuzzy Systems
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