Proposes neuro-quantum-fuzzy systems via quantum-neural networks to enable simultaneous probabilistic and crisp inference in ontology-based knowledge representation.
Onto2Vec: joint vector-based representation of biological entities and their ontology-based annotations
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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.
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cs.AI 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
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Extending Ontologies: From Dense Embeddings to Hybrid Quantum-Fuzzy Systems
Proposes neuro-quantum-fuzzy systems via quantum-neural networks to enable simultaneous probabilistic and crisp inference in ontology-based knowledge representation.