entity Search Results


registrant country entity name jurisdiction inbrand limited inbrand uk limited karner europe  (Mallinckrodt)
86
Mallinckrodt registrant country entity name jurisdiction inbrand limited inbrand uk limited karner europe
Registrant Country Entity Name Jurisdiction Inbrand Limited Inbrand Uk Limited Karner Europe, supplied by Mallinckrodt, used in various techniques. Bioz Stars score: 86/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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Average 86 stars, based on 1 article reviews
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nct01907100 legal entity  (Boehringer Ingelheim)
86
Boehringer Ingelheim nct01907100 legal entity
Nct01907100 Legal Entity, supplied by Boehringer Ingelheim, used in various techniques. Bioz Stars score: 86/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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nct00540514 legal entity  (Celgene)
86
Celgene nct00540514 legal entity
Nct00540514 Legal Entity, supplied by Celgene, used in various techniques. Bioz Stars score: 86/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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qualified provider-led entity/appropriate imaging coalition  (Siemens AG)
90
Siemens AG qualified provider-led entity/appropriate imaging coalition
Qualified Provider Led Entity/Appropriate Imaging Coalition, supplied by Siemens AG, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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Average 90 stars, based on 1 article reviews
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molecular entity  (Biopharm GmbH)
90
Biopharm GmbH molecular entity
Molecular Entity, supplied by Biopharm GmbH, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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Average 90 stars, based on 1 article reviews
molecular entity - by Bioz Stars, 2026-05
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chemical entity mention recognition  (CEM Corporation)
90
CEM Corporation chemical entity mention recognition
Chemical Entity Mention Recognition, supplied by CEM Corporation, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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Average 90 stars, based on 1 article reviews
chemical entity mention recognition - by Bioz Stars, 2026-05
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entity-attribute-value model with classes and relationships (eav/cr)  (Human Brain Project)
90
Human Brain Project entity-attribute-value model with classes and relationships (eav/cr)
Entity Attribute Value Model With Classes And Relationships (Eav/Cr), supplied by Human Brain Project, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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Average 90 stars, based on 1 article reviews
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multicell/multicast coordination entity (mce) 42  (MultiCell Technologies)
90
MultiCell Technologies multicell/multicast coordination entity (mce) 42
Multicell/Multicast Coordination Entity (Mce) 42, supplied by MultiCell Technologies, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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Average 90 stars, based on 1 article reviews
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named entity normalization  (NEN Life Science)
90
NEN Life Science named entity normalization
Flow chart of a typical text-mining system. Literature mining systems can be roughly divided into base level and high level. At the base level, there are three important tasks, including named entity <t>recognition</t> <t>(NER),</t> named entity normalization (NEN) and relation extraction detection (RE). Topic recognition (TR), knowledge discovery (KD) and database curation (DC) are handled at the high level.
Named Entity Normalization, supplied by NEN Life Science, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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graph-based hierarchical medical entity embedding  (IQVIA Inc)
90
IQVIA Inc graph-based hierarchical medical entity embedding
Flow chart of a typical text-mining system. Literature mining systems can be roughly divided into base level and high level. At the base level, there are three important tasks, including named entity <t>recognition</t> <t>(NER),</t> named entity normalization (NEN) and relation extraction detection (RE). Topic recognition (TR), knowledge discovery (KD) and database curation (DC) are handled at the high level.
Graph Based Hierarchical Medical Entity Embedding, supplied by IQVIA Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/result/graph-based hierarchical medical entity embedding/product/IQVIA Inc
Average 90 stars, based on 1 article reviews
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ng3 user plane interface  (NextGen Sciences)
90
NextGen Sciences ng3 user plane interface
Flow chart of a typical text-mining system. Literature mining systems can be roughly divided into base level and high level. At the base level, there are three important tasks, including named entity <t>recognition</t> <t>(NER),</t> named entity normalization (NEN) and relation extraction detection (RE). Topic recognition (TR), knowledge discovery (KD) and database curation (DC) are handled at the high level.
Ng3 User Plane Interface, supplied by NextGen Sciences, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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Average 90 stars, based on 1 article reviews
ng3 user plane interface - by Bioz Stars, 2026-05
90/100 stars
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nonprofi t entity  (McDonnell Douglas Corporation)
90
McDonnell Douglas Corporation nonprofi t entity
Flow chart of a typical text-mining system. Literature mining systems can be roughly divided into base level and high level. At the base level, there are three important tasks, including named entity <t>recognition</t> <t>(NER),</t> named entity normalization (NEN) and relation extraction detection (RE). Topic recognition (TR), knowledge discovery (KD) and database curation (DC) are handled at the high level.
Nonprofi T Entity, supplied by McDonnell Douglas Corporation, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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Average 90 stars, based on 1 article reviews
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Image Search Results


Flow chart of a typical text-mining system. Literature mining systems can be roughly divided into base level and high level. At the base level, there are three important tasks, including named entity recognition (NER), named entity normalization (NEN) and relation extraction detection (RE). Topic recognition (TR), knowledge discovery (KD) and database curation (DC) are handled at the high level.

Journal: Molecules

Article Title: Literature Mining of Disease Associated Noncoding RNA in the Omics Era

doi: 10.3390/molecules27154710

Figure Lengend Snippet: Flow chart of a typical text-mining system. Literature mining systems can be roughly divided into base level and high level. At the base level, there are three important tasks, including named entity recognition (NER), named entity normalization (NEN) and relation extraction detection (RE). Topic recognition (TR), knowledge discovery (KD) and database curation (DC) are handled at the high level.

Article Snippet: At the base level, there are three important tasks: named entity recognition (NER), named entity normalization (NEN) and relation extraction (RE).

Techniques: Extraction