Matches in SemOpenAlex for { <https://semopenalex.org/work/W4323042319> ?p ?o ?g. }
Showing items 1 to 81 of
81
with 100 items per page.
- W4323042319 abstract "Determining the side effects of multidrug combinations is a very important issue in drug risk studies. However, designing clinical trials to determine frequencies is often time-consuming and expensive, and previous work has often been limited to using the target protein of a drug without screening. Although this alleviates the sparsity of the raw data to some extent, blindly introducing proteins as auxiliary information can lead to a large amount of noisy information being added, which in turn leads to less efficient models. For this reason, we propose a new method called Gorge (graph convolutional networks on heterogeneous multi-relational graphs for polypharmacy side effect prediction). Specifically, we designed two protein auxiliary pathways directly related to drugs and combined these two auxiliary pathways with a multi-relational graph of drug side effects, which both alleviates data sparsity and filters noisy data. Then, we introduce a query-aware attention mechanism that generates different attention pathways for drug entities based on different drug pairs, fine-grained to determine the extent of information delivery. Finally, we output the exact frequency of drug side effects occurring through a tensor factorization decoder, in contrast to most existing methods that can only predict the presence or association of side effects, but not their frequency. We found that Gorge achieves excellent performance on real-world datasets (average AUROC of 0.822 and average AUPR of 0.775), outperforming existing methods. Further examination provides literature evidence for highly ranked predictions." @default.
- W4323042319 created "2023-03-04" @default.
- W4323042319 creator A5015955860 @default.
- W4323042319 creator A5041114760 @default.
- W4323042319 creator A5053794277 @default.
- W4323042319 creator A5080228341 @default.
- W4323042319 date "2023-03-03" @default.
- W4323042319 modified "2023-10-16" @default.
- W4323042319 title "Gorge: graph convolutional networks on heterogeneous multi-relational graphs for polypharmacy side effect prediction" @default.
- W4323042319 cites W2097308346 @default.
- W4323042319 cites W2145578524 @default.
- W4323042319 cites W2145877930 @default.
- W4323042319 cites W2214187833 @default.
- W4323042319 cites W2294516783 @default.
- W4323042319 cites W2591704587 @default.
- W4323042319 cites W2619647479 @default.
- W4323042319 cites W2755271241 @default.
- W4323042319 cites W2786016794 @default.
- W4323042319 cites W2900758217 @default.
- W4323042319 cites W3030186245 @default.
- W4323042319 cites W3039010238 @default.
- W4323042319 cites W3103362336 @default.
- W4323042319 cites W3104097132 @default.
- W4323042319 cites W3156814830 @default.
- W4323042319 cites W3216291532 @default.
- W4323042319 cites W4200042293 @default.
- W4323042319 cites W4200080698 @default.
- W4323042319 cites W4213164841 @default.
- W4323042319 cites W4213235856 @default.
- W4323042319 cites W4307093167 @default.
- W4323042319 cites W4312963085 @default.
- W4323042319 doi "https://doi.org/10.1007/s43674-023-00053-3" @default.
- W4323042319 hasPublicationYear "2023" @default.
- W4323042319 type Work @default.
- W4323042319 citedByCount "0" @default.
- W4323042319 crossrefType "journal-article" @default.
- W4323042319 hasAuthorship W4323042319A5015955860 @default.
- W4323042319 hasAuthorship W4323042319A5041114760 @default.
- W4323042319 hasAuthorship W4323042319A5053794277 @default.
- W4323042319 hasAuthorship W4323042319A5080228341 @default.
- W4323042319 hasBestOaLocation W43230423191 @default.
- W4323042319 hasConcept C119857082 @default.
- W4323042319 hasConcept C124101348 @default.
- W4323042319 hasConcept C126322002 @default.
- W4323042319 hasConcept C132525143 @default.
- W4323042319 hasConcept C199360897 @default.
- W4323042319 hasConcept C3454156 @default.
- W4323042319 hasConcept C36434225 @default.
- W4323042319 hasConcept C41008148 @default.
- W4323042319 hasConcept C71924100 @default.
- W4323042319 hasConcept C80444323 @default.
- W4323042319 hasConceptScore W4323042319C119857082 @default.
- W4323042319 hasConceptScore W4323042319C124101348 @default.
- W4323042319 hasConceptScore W4323042319C126322002 @default.
- W4323042319 hasConceptScore W4323042319C132525143 @default.
- W4323042319 hasConceptScore W4323042319C199360897 @default.
- W4323042319 hasConceptScore W4323042319C3454156 @default.
- W4323042319 hasConceptScore W4323042319C36434225 @default.
- W4323042319 hasConceptScore W4323042319C41008148 @default.
- W4323042319 hasConceptScore W4323042319C71924100 @default.
- W4323042319 hasConceptScore W4323042319C80444323 @default.
- W4323042319 hasFunder F4320321001 @default.
- W4323042319 hasFunder F4320327602 @default.
- W4323042319 hasIssue "2" @default.
- W4323042319 hasLocation W43230423191 @default.
- W4323042319 hasOpenAccess W4323042319 @default.
- W4323042319 hasPrimaryLocation W43230423191 @default.
- W4323042319 hasRelatedWork W2031585521 @default.
- W4323042319 hasRelatedWork W2146607889 @default.
- W4323042319 hasRelatedWork W2347219288 @default.
- W4323042319 hasRelatedWork W2366221835 @default.
- W4323042319 hasRelatedWork W2767763205 @default.
- W4323042319 hasRelatedWork W2889453578 @default.
- W4323042319 hasRelatedWork W2950989280 @default.
- W4323042319 hasRelatedWork W3157984467 @default.
- W4323042319 hasRelatedWork W4289373237 @default.
- W4323042319 hasRelatedWork W4385718306 @default.
- W4323042319 hasVolume "3" @default.
- W4323042319 isParatext "false" @default.
- W4323042319 isRetracted "false" @default.
- W4323042319 workType "article" @default.