Matches in SemOpenAlex for { <https://semopenalex.org/work/W4220796987> ?p ?o ?g. }
Showing items 1 to 99 of
99
with 100 items per page.
- W4220796987 endingPage "2854" @default.
- W4220796987 startingPage "2847" @default.
- W4220796987 abstract "Identification of Drug-Target Interactions (DTIs) is an essential step in drug discovery and repositioning. DTI prediction based on biological experiments is time-consuming and expensive. In recent years, graph learning-based methods have aroused widespread interest and shown certain advantages on this task, where the DTI prediction is often modeled as a binary classification problem of the nodes composed of drug and protein pairs (DPPs). Nevertheless, in many real applications, labeled data are very limited and expensive to obtain. With only a few thousand labeled data, models could hardly recognize comprehensive patterns of DPP node representations, and are unable to capture enough commonsense knowledge, which is required in DTI prediction. Supervised contrastive learning gives an aligned representation of DPP node representations with the same class label. In embedding space, DPP node representations with the same label are pulled together, and those with different labels are pushed apart.We propose an end-to-end supervised graph co-contrastive learning model for DTI prediction directly from heterogeneous networks. By contrasting the topology structures and semantic features of the drug-protein-pair network, as well as the new selection strategy of positive and negative samples, SGCL-DTI generates a contrastive loss to guide the model optimization in a supervised manner. Comprehensive experiments on three public datasets demonstrate that our model outperforms the SOTA methods significantly on the task of DTI prediction, especially in the case of cold start. Furthermore, SGCL-DTI provides a new research perspective of contrastive learning for DTI prediction.The research shows that this method has certain applicability in the discovery of drugs, the identification of drug-target pairs and so on." @default.
- W4220796987 created "2022-04-03" @default.
- W4220796987 creator A5009607229 @default.
- W4220796987 creator A5024962900 @default.
- W4220796987 creator A5044463892 @default.
- W4220796987 creator A5061898108 @default.
- W4220796987 date "2022-03-21" @default.
- W4220796987 modified "2023-10-14" @default.
- W4220796987 title "Supervised graph co-contrastive learning for drug–target interaction prediction" @default.
- W4220796987 cites W1984084871 @default.
- W4220796987 cites W1998898494 @default.
- W4220796987 cites W2066201825 @default.
- W4220796987 cites W2094791588 @default.
- W4220796987 cites W2137632714 @default.
- W4220796987 cites W2139516171 @default.
- W4220796987 cites W2152454589 @default.
- W4220796987 cites W2162011385 @default.
- W4220796987 cites W2278874757 @default.
- W4220796987 cites W2326704759 @default.
- W4220796987 cites W2336207313 @default.
- W4220796987 cites W2592742128 @default.
- W4220796987 cites W2623587811 @default.
- W4220796987 cites W2729788619 @default.
- W4220796987 cites W2753953057 @default.
- W4220796987 cites W2887766329 @default.
- W4220796987 cites W2945027804 @default.
- W4220796987 cites W2948035163 @default.
- W4220796987 cites W3013646438 @default.
- W4220796987 cites W3021338900 @default.
- W4220796987 cites W3085429933 @default.
- W4220796987 cites W3126516421 @default.
- W4220796987 cites W3163972593 @default.
- W4220796987 doi "https://doi.org/10.1093/bioinformatics/btac164" @default.
- W4220796987 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/35561181" @default.
- W4220796987 hasPublicationYear "2022" @default.
- W4220796987 type Work @default.
- W4220796987 citedByCount "14" @default.
- W4220796987 countsByYear W42207969872022 @default.
- W4220796987 countsByYear W42207969872023 @default.
- W4220796987 crossrefType "journal-article" @default.
- W4220796987 hasAuthorship W4220796987A5009607229 @default.
- W4220796987 hasAuthorship W4220796987A5024962900 @default.
- W4220796987 hasAuthorship W4220796987A5044463892 @default.
- W4220796987 hasAuthorship W4220796987A5061898108 @default.
- W4220796987 hasConcept C119857082 @default.
- W4220796987 hasConcept C12267149 @default.
- W4220796987 hasConcept C127413603 @default.
- W4220796987 hasConcept C132525143 @default.
- W4220796987 hasConcept C153180895 @default.
- W4220796987 hasConcept C154945302 @default.
- W4220796987 hasConcept C2989108626 @default.
- W4220796987 hasConcept C41008148 @default.
- W4220796987 hasConcept C41608201 @default.
- W4220796987 hasConcept C62611344 @default.
- W4220796987 hasConcept C66905080 @default.
- W4220796987 hasConcept C66938386 @default.
- W4220796987 hasConcept C71924100 @default.
- W4220796987 hasConcept C75564084 @default.
- W4220796987 hasConcept C80444323 @default.
- W4220796987 hasConcept C98274493 @default.
- W4220796987 hasConceptScore W4220796987C119857082 @default.
- W4220796987 hasConceptScore W4220796987C12267149 @default.
- W4220796987 hasConceptScore W4220796987C127413603 @default.
- W4220796987 hasConceptScore W4220796987C132525143 @default.
- W4220796987 hasConceptScore W4220796987C153180895 @default.
- W4220796987 hasConceptScore W4220796987C154945302 @default.
- W4220796987 hasConceptScore W4220796987C2989108626 @default.
- W4220796987 hasConceptScore W4220796987C41008148 @default.
- W4220796987 hasConceptScore W4220796987C41608201 @default.
- W4220796987 hasConceptScore W4220796987C62611344 @default.
- W4220796987 hasConceptScore W4220796987C66905080 @default.
- W4220796987 hasConceptScore W4220796987C66938386 @default.
- W4220796987 hasConceptScore W4220796987C71924100 @default.
- W4220796987 hasConceptScore W4220796987C75564084 @default.
- W4220796987 hasConceptScore W4220796987C80444323 @default.
- W4220796987 hasConceptScore W4220796987C98274493 @default.
- W4220796987 hasFunder F4320313614 @default.
- W4220796987 hasFunder F4320321001 @default.
- W4220796987 hasIssue "10" @default.
- W4220796987 hasLocation W42207969871 @default.
- W4220796987 hasLocation W42207969872 @default.
- W4220796987 hasOpenAccess W4220796987 @default.
- W4220796987 hasPrimaryLocation W42207969871 @default.
- W4220796987 hasRelatedWork W3035116611 @default.
- W4220796987 hasRelatedWork W3044354590 @default.
- W4220796987 hasRelatedWork W3120440802 @default.
- W4220796987 hasRelatedWork W3133593829 @default.
- W4220796987 hasRelatedWork W3215867059 @default.
- W4220796987 hasRelatedWork W4212923699 @default.
- W4220796987 hasRelatedWork W4287763734 @default.
- W4220796987 hasRelatedWork W4322008322 @default.
- W4220796987 hasRelatedWork W4328134586 @default.
- W4220796987 hasRelatedWork W2345184372 @default.
- W4220796987 hasVolume "38" @default.
- W4220796987 isParatext "false" @default.
- W4220796987 isRetracted "false" @default.
- W4220796987 workType "article" @default.