Matches in SemOpenAlex for { <https://semopenalex.org/work/W4211228052> ?p ?o ?g. }
Showing items 1 to 76 of
76
with 100 items per page.
- W4211228052 abstract "Abstract DNA methylation data-based precision tumour early diagnostics is emerging as the state of the art for molecular tumour recognition, which could capture the signals of cancer occurrence 3 ~ 5 years in advance and clinically more homogenous groups. However, the sensitive of early detection for many tumors is about 30%, which needs to be greatly improved. Nevertheless, on the basis of the whole genome bisulfite sequencing methylation data, a comprehensive characterisation of the entire molecular genetic landscape of the tumor as well as the subtle differences between different tumours could be identified. With the accumulation of methylation data, high performance deep learning models that considering and modeling more unbiased information need to be developed. According to the above analysis, we designed a pipeline to investigate genome-wide DNA methylation patterns for precision multi-tumour early diagnostics. We proposed a graph convolutional network considering the attention mechanism to dissect DNA methylation heterogeneity of different cancer types. The attention mechanism in the graph convolutional network architecture could detect the cancer signals from genome-wide methylated molecular interactions, improving the robustness and sensitivity of multi-tumor classification. Experimental results demonstrates the feasibility of precision multi-tumour early diagnostics with the DNA methylation data. The workflow presented here is very useful for tumor classification, which highly relevant for the future blood diagnosis and treatment of the tumour." @default.
- W4211228052 created "2022-02-13" @default.
- W4211228052 creator A5013388466 @default.
- W4211228052 creator A5013911017 @default.
- W4211228052 creator A5014672057 @default.
- W4211228052 creator A5017107904 @default.
- W4211228052 creator A5048020168 @default.
- W4211228052 creator A5062315365 @default.
- W4211228052 creator A5068701024 @default.
- W4211228052 creator A5072570597 @default.
- W4211228052 creator A5075157579 @default.
- W4211228052 creator A5076043452 @default.
- W4211228052 creator A5081142384 @default.
- W4211228052 creator A5091803900 @default.
- W4211228052 date "2022-02-11" @default.
- W4211228052 modified "2023-10-16" @default.
- W4211228052 title "A Self-attention Graph Convolutional Network for Precision Multi-tumour Early Diagnostics with DNA Methylation Data" @default.
- W4211228052 doi "https://doi.org/10.21203/rs.3.rs-1348334/v1" @default.
- W4211228052 hasPublicationYear "2022" @default.
- W4211228052 type Work @default.
- W4211228052 citedByCount "0" @default.
- W4211228052 crossrefType "posted-content" @default.
- W4211228052 hasAuthorship W4211228052A5013388466 @default.
- W4211228052 hasAuthorship W4211228052A5013911017 @default.
- W4211228052 hasAuthorship W4211228052A5014672057 @default.
- W4211228052 hasAuthorship W4211228052A5017107904 @default.
- W4211228052 hasAuthorship W4211228052A5048020168 @default.
- W4211228052 hasAuthorship W4211228052A5062315365 @default.
- W4211228052 hasAuthorship W4211228052A5068701024 @default.
- W4211228052 hasAuthorship W4211228052A5072570597 @default.
- W4211228052 hasAuthorship W4211228052A5075157579 @default.
- W4211228052 hasAuthorship W4211228052A5076043452 @default.
- W4211228052 hasAuthorship W4211228052A5081142384 @default.
- W4211228052 hasAuthorship W4211228052A5091803900 @default.
- W4211228052 hasBestOaLocation W42112280521 @default.
- W4211228052 hasConcept C104317684 @default.
- W4211228052 hasConcept C121912465 @default.
- W4211228052 hasConcept C132525143 @default.
- W4211228052 hasConcept C150194340 @default.
- W4211228052 hasConcept C190727270 @default.
- W4211228052 hasConcept C33288867 @default.
- W4211228052 hasConcept C41008148 @default.
- W4211228052 hasConcept C54355233 @default.
- W4211228052 hasConcept C552990157 @default.
- W4211228052 hasConcept C70721500 @default.
- W4211228052 hasConcept C80444323 @default.
- W4211228052 hasConcept C86803240 @default.
- W4211228052 hasConceptScore W4211228052C104317684 @default.
- W4211228052 hasConceptScore W4211228052C121912465 @default.
- W4211228052 hasConceptScore W4211228052C132525143 @default.
- W4211228052 hasConceptScore W4211228052C150194340 @default.
- W4211228052 hasConceptScore W4211228052C190727270 @default.
- W4211228052 hasConceptScore W4211228052C33288867 @default.
- W4211228052 hasConceptScore W4211228052C41008148 @default.
- W4211228052 hasConceptScore W4211228052C54355233 @default.
- W4211228052 hasConceptScore W4211228052C552990157 @default.
- W4211228052 hasConceptScore W4211228052C70721500 @default.
- W4211228052 hasConceptScore W4211228052C80444323 @default.
- W4211228052 hasConceptScore W4211228052C86803240 @default.
- W4211228052 hasLocation W42112280521 @default.
- W4211228052 hasLocation W42112280522 @default.
- W4211228052 hasOpenAccess W4211228052 @default.
- W4211228052 hasPrimaryLocation W42112280521 @default.
- W4211228052 hasRelatedWork W10118400 @default.
- W4211228052 hasRelatedWork W1682090 @default.
- W4211228052 hasRelatedWork W1900789 @default.
- W4211228052 hasRelatedWork W2047947 @default.
- W4211228052 hasRelatedWork W2251229 @default.
- W4211228052 hasRelatedWork W3477976 @default.
- W4211228052 hasRelatedWork W4153007 @default.
- W4211228052 hasRelatedWork W7320618 @default.
- W4211228052 hasRelatedWork W805638 @default.
- W4211228052 hasRelatedWork W970881 @default.
- W4211228052 isParatext "false" @default.
- W4211228052 isRetracted "false" @default.
- W4211228052 workType "article" @default.