Matches in SemOpenAlex for { <https://semopenalex.org/work/W3045642779> ?p ?o ?g. }
Showing items 1 to 100 of
100
with 100 items per page.
- W3045642779 endingPage "242" @default.
- W3045642779 startingPage "236" @default.
- W3045642779 abstract "Abstract Motivation The automatic extraction of published relationships between molecular entities has important applications in many biomedical fields, ranging from Systems Biology to Personalized Medicine. Existing works focused on extracting relationships described in single articles or in single sentences. However, a single record is rarely sufficient to judge upon the biological correctness of a relation, as experimental evidence might be weak or only valid in a certain context. Furthermore, statements may be more speculative than confirmative, and different articles often contradict each other. Experts therefore always take the complete literature into account to take a reliable decision upon a relationship. It is an open research question how to do this effectively in an automatic manner. Results We propose two novel relation extraction approaches which use recent representation learning techniques to create comprehensive models of biomedical entities or entity-pairs, respectively. These representations are learned by considering all publications from PubMed mentioning an entity or a pair. They are used as input for a neural network for classifying relations globally, i.e. the derived predictions are corpus-based, not sentence- or article based as in prior art. Experiments on the extraction of mutation–disease, drug–disease and drug–drug relationships show that the learned embeddings indeed capture semantic information of the entities under study and outperform traditional methods by 4–29% regarding F1 score. Availability and implementation Source codes are available at: https://github.com/mariosaenger/bio-re-with-entity-embeddings. Supplementary information Supplementary data are available at Bioinformatics online." @default.
- W3045642779 created "2020-08-03" @default.
- W3045642779 creator A5017484078 @default.
- W3045642779 creator A5055236937 @default.
- W3045642779 date "2020-07-29" @default.
- W3045642779 modified "2023-09-23" @default.
- W3045642779 title "Large-scale entity representation learning for biomedical relationship extraction" @default.
- W3045642779 cites W1964670939 @default.
- W3045642779 cites W2016928406 @default.
- W3045642779 cites W2048059249 @default.
- W3045642779 cites W2116759194 @default.
- W3045642779 cites W2118919822 @default.
- W3045642779 cites W2128803939 @default.
- W3045642779 cites W2142016317 @default.
- W3045642779 cites W2164394614 @default.
- W3045642779 cites W2485374661 @default.
- W3045642779 cites W2526413677 @default.
- W3045642779 cites W2583911935 @default.
- W3045642779 cites W2767891136 @default.
- W3045642779 cites W2793745141 @default.
- W3045642779 cites W2806387533 @default.
- W3045642779 cites W2911489562 @default.
- W3045642779 cites W2916047525 @default.
- W3045642779 cites W2967346896 @default.
- W3045642779 doi "https://doi.org/10.1093/bioinformatics/btaa674" @default.
- W3045642779 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/32726411" @default.
- W3045642779 hasPublicationYear "2020" @default.
- W3045642779 type Work @default.
- W3045642779 sameAs 3045642779 @default.
- W3045642779 citedByCount "5" @default.
- W3045642779 countsByYear W30456427792022 @default.
- W3045642779 countsByYear W30456427792023 @default.
- W3045642779 crossrefType "journal-article" @default.
- W3045642779 hasAuthorship W3045642779A5017484078 @default.
- W3045642779 hasAuthorship W3045642779A5055236937 @default.
- W3045642779 hasConcept C119857082 @default.
- W3045642779 hasConcept C124101348 @default.
- W3045642779 hasConcept C151730666 @default.
- W3045642779 hasConcept C153604712 @default.
- W3045642779 hasConcept C154945302 @default.
- W3045642779 hasConcept C165141518 @default.
- W3045642779 hasConcept C17744445 @default.
- W3045642779 hasConcept C195807954 @default.
- W3045642779 hasConcept C199360897 @default.
- W3045642779 hasConcept C199539241 @default.
- W3045642779 hasConcept C204321447 @default.
- W3045642779 hasConcept C23123220 @default.
- W3045642779 hasConcept C2522767166 @default.
- W3045642779 hasConcept C25343380 @default.
- W3045642779 hasConcept C2776359362 @default.
- W3045642779 hasConcept C2777530160 @default.
- W3045642779 hasConcept C2779343474 @default.
- W3045642779 hasConcept C41008148 @default.
- W3045642779 hasConcept C55439883 @default.
- W3045642779 hasConcept C71472368 @default.
- W3045642779 hasConcept C86803240 @default.
- W3045642779 hasConcept C94625758 @default.
- W3045642779 hasConceptScore W3045642779C119857082 @default.
- W3045642779 hasConceptScore W3045642779C124101348 @default.
- W3045642779 hasConceptScore W3045642779C151730666 @default.
- W3045642779 hasConceptScore W3045642779C153604712 @default.
- W3045642779 hasConceptScore W3045642779C154945302 @default.
- W3045642779 hasConceptScore W3045642779C165141518 @default.
- W3045642779 hasConceptScore W3045642779C17744445 @default.
- W3045642779 hasConceptScore W3045642779C195807954 @default.
- W3045642779 hasConceptScore W3045642779C199360897 @default.
- W3045642779 hasConceptScore W3045642779C199539241 @default.
- W3045642779 hasConceptScore W3045642779C204321447 @default.
- W3045642779 hasConceptScore W3045642779C23123220 @default.
- W3045642779 hasConceptScore W3045642779C2522767166 @default.
- W3045642779 hasConceptScore W3045642779C25343380 @default.
- W3045642779 hasConceptScore W3045642779C2776359362 @default.
- W3045642779 hasConceptScore W3045642779C2777530160 @default.
- W3045642779 hasConceptScore W3045642779C2779343474 @default.
- W3045642779 hasConceptScore W3045642779C41008148 @default.
- W3045642779 hasConceptScore W3045642779C55439883 @default.
- W3045642779 hasConceptScore W3045642779C71472368 @default.
- W3045642779 hasConceptScore W3045642779C86803240 @default.
- W3045642779 hasConceptScore W3045642779C94625758 @default.
- W3045642779 hasIssue "2" @default.
- W3045642779 hasLocation W30456427791 @default.
- W3045642779 hasOpenAccess W3045642779 @default.
- W3045642779 hasPrimaryLocation W30456427791 @default.
- W3045642779 hasRelatedWork W150863218 @default.
- W3045642779 hasRelatedWork W1517743118 @default.
- W3045642779 hasRelatedWork W159132833 @default.
- W3045642779 hasRelatedWork W2368651715 @default.
- W3045642779 hasRelatedWork W2572241437 @default.
- W3045642779 hasRelatedWork W2791647956 @default.
- W3045642779 hasRelatedWork W2791684315 @default.
- W3045642779 hasRelatedWork W2883385582 @default.
- W3045642779 hasRelatedWork W3144259685 @default.
- W3045642779 hasRelatedWork W4200223488 @default.
- W3045642779 hasVolume "37" @default.
- W3045642779 isParatext "false" @default.
- W3045642779 isRetracted "false" @default.
- W3045642779 magId "3045642779" @default.
- W3045642779 workType "article" @default.