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Multi-ontology embeddings approach on human-aligned multi-ontologies representation for gene-disease associations prediction paper
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Paper

Multi-ontology embeddings approach on human-aligned multi-ontologies representation for gene-disease associations prediction

01a2fe143a05b9faeb481f86261dd1f0db3a57fa
Free2AITools Nexus Index
62.3
S: Semantic 50

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A: Authority 68
P: Popularity 43
R: Recency 100
Q: Quality 65
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Objectives Knowledge graphs and ontologies in the biomedical domain provide rich contextual knowledge for a variety of challenges. Employing that for knowledge-driven NLP tasks such as gene-disease association prediction represents a promising way to increase the predictive power of a model. Methods We investigated the power of infusing the embedding of two aligned ontologies as prior knowledge to the NLP models. We evaluated the performance of different models on some large-scale gene-diseas...

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Registry ID 01a2fe143a05b9faeb481f86261dd1f0db3a57fa
License ArXiv
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BibTeX 
@misc{01a2fe143a05b9faeb481f86261dd1f0db3a57fa,
  title = {Multi-ontology embeddings approach on human-aligned multi-ontologies representation for gene-disease associations prediction Paper},
  howpublished = {\url{https://api.semanticscholar.org/01a2fe143a05b9faeb481f86261dd1f0db3a57fa}},
  note = {Accessed via Free2AITools.}
}
APA Style
Multi-ontology embeddings approach on human-aligned multi-ontologies representation for gene-disease associations prediction [Paper]. Free2AITools. https://api.semanticscholar.org/01a2fe143a05b9faeb481f86261dd1f0db3a57fa

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Semantic (S) 50

Query-time baseline · scored live at search

Authority (A) 68
Popularity (P) 43
Recency (R) 100
Quality (Q) 65

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FNI V2.0 for Multi-ontology embeddings approach on human-aligned multi-ontologies representation for gene-disease associations prediction: Authority (A:68), Popularity (P:43), Recency (R:100), Quality (Q:65). Semantic (S) is a query-time baseline scored live at search.

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πŸ“ Executive Summary

"Objectives Knowledge graphs and ontologies in the biomedical domain provide rich contextual knowledge for a variety of challenges. Employing that for knowledge-driven NLP tasks such as gene-disease association prediction represents a promising way to increase the predictive power of a model. Methods We investigated the power of infusing the embedding of two aligned ontologies as prior knowledge to the NLP models. We evaluated the performance of different models on some large-scale gene-diseas..."

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@article{Multiontology,
  title={Multi-ontology embeddings approach on human-aligned multi-ontologies representation for gene-disease associations prediction},
  note={Indexed by Free2AITools}
}

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πŸ“ˆ3CitationsSemantic Scholar
πŸ›οΈ68AuthorityFNI pillar
⏱️100RecencyFNI pillar
βœ…65QualityFNI pillar
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embeddings
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