Predicting drug characteristics using biomedical text embedding

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Location: Deutsche Nationalbibliothek Frankfurt am Main

ISSN: 1471-2105

Extent: Online-Ressource

Language: Englisch

Notes: online resource.

Bibliographic citation: Predicting drug characteristics using biomedical text embedding ; volume:23 ; number:1 ; day:7 ; month:12 ; year:2022 ; pages:1-17 ; date:12.2022
BMC bioinformatics ; 23, Heft 1 (7.12.2022), 1-17, 12.2022

Creator: Shtar, Guy
Greenstein-Messica, Asnat
Mazuz, Eyal
Rokach, Lior
Shapira, Bracha

Contributor: SpringerLink (Online service)

DOI: 10.1186/s12859-022-05083-1

URN: urn:nbn:de:101:1-2023021721081644037414

Rights: Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.

Last update: 14.08.2025, 10:49 AM CEST

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Shtar, Guy
Greenstein-Messica, Asnat
Mazuz, Eyal
Rokach, Lior
Shapira, Bracha
SpringerLink (Online service)

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Predicting drug characteristics using biomedical text embedding

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Other Objects (12)

NEDD: a network embedding based method for predicting drug-disease associations

Predicting combinations of drugs by exploiting graph embedding of heterogeneous networks

Biomedical event trigger detection by dependency-based word embedding

Fast and scalable neural embedding models for biomedical sentence classification

Graph embedding on mass spectrometry- and sequencing-based biomedical data

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Concept embedding-based weighting scheme for biomedical text clustering and visualization

EOESGC: predicting miRNA-disease associations based on embedding of embedding and simplified graph convolutional network

Neural sentence embedding models for semantic similarity estimation in the biomedical domain

Predicting disease-related genes using integrated biomedical networks

Consensus embedding: theory, algorithms and application to segmentation and classification of biomedical data

Embedding covariate adjustments in tree-based automated machine learning for biomedical big data analyses

NEDD: a network embedding based method for predicting drug-disease associations

Predicting combinations of drugs by exploiting graph embedding of heterogeneous networks

Biomedical event trigger detection by dependency-based word embedding

Fast and scalable neural embedding models for biomedical sentence classification

Graph embedding on mass spectrometry- and sequencing-based biomedical data

Extracting drug-drug interactions from biomedical texts

Concept embedding-based weighting scheme for biomedical text clustering and visualization

EOESGC: predicting miRNA-disease associations based on embedding of embedding and simplified graph convolutional network

Neural sentence embedding models for semantic similarity estimation in the biomedical domain

Predicting disease-related genes using integrated biomedical networks

Consensus embedding: theory, algorithms and application to segmentation and classification of biomedical data

Embedding covariate adjustments in tree-based automated machine learning for biomedical big data analyses

NEDD: a network embedding based method for predicting drug-disease associations

Predicting combinations of drugs by exploiting graph embedding of heterogeneous networks

Biomedical event trigger detection by dependency-based word embedding

Fast and scalable neural embedding models for biomedical sentence classification

Graph embedding on mass spectrometry- and sequencing-based biomedical data

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Concept embedding-based weighting scheme for biomedical text clustering and visualization

EOESGC: predicting miRNA-disease associations based on embedding of embedding and simplified graph convolutional network

Neural sentence embedding models for semantic similarity estimation in the biomedical domain

Predicting disease-related genes using integrated biomedical networks

Consensus embedding: theory, algorithms and application to segmentation and classification of biomedical data

Embedding covariate adjustments in tree-based automated machine learning for biomedical big data analyses

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