Matches in SemOpenAlex for { <https://semopenalex.org/work/W4323981307> ?p ?o ?g. }
Showing items 1 to 84 of
84
with 100 items per page.
- W4323981307 abstract "Abstract Prostate cancer is one of the deadliest cancers worldwide. An accurate prediction of pathological stages using the expressions and interactions of genes is effective for clinical assessment and treatment. However, identification of interactions using biological procedure is time consuming and prohibitively expensive. A graph is a powerful representation for the complex interactome of genes, their transcripts, and proteins. Recently, Graph Neural Networks (GNNs) have gained great attention in machine learning due to their capability to capture the graphical interactions among data entities. To leverage GNNs for predicting pathological stage stages, we developed an end-to-end graph representation and learning model, namely E2EGraph, which can automatically generate a graph representation using gene expression data and a multi-head graph attention network to learn the strength of interactions among genes and make the prediction. To ensure the reliability of model prediction, we identify critical components of graph representation and GNN model to interpret prediction results from multiple perspectives at gene and patient levels. We evaluated E2EGraph to predict pathological stages of prostate cancer using The Cancer Genome Atlas (TCGA) data. Our experimental results demonstrate that E2EGraph reaches the state-of-art prediction performance while being effective in identifying marker genes indicated by interpretability. Our results point to a direction where adaptive graph construction and attention based GNNs can be leveraged for various prediction tasks and interpretation of model prediction in a variety of data domains including disease prediction." @default.
- W4323981307 created "2023-03-13" @default.
- W4323981307 creator A5001760759 @default.
- W4323981307 creator A5008597409 @default.
- W4323981307 creator A5052217203 @default.
- W4323981307 creator A5055295290 @default.
- W4323981307 creator A5074868327 @default.
- W4323981307 date "2023-03-12" @default.
- W4323981307 modified "2023-10-18" @default.
- W4323981307 title "E2EGraph: An End-to-end Graph Learning Model for Interpretable Prediction of Pathlogical Stages in Prostate Cancer" @default.
- W4323981307 cites W1960669649 @default.
- W4323981307 cites W1977391294 @default.
- W4323981307 cites W1978761216 @default.
- W4323981307 cites W1987776395 @default.
- W4323981307 cites W2002384741 @default.
- W4323981307 cites W2048701123 @default.
- W4323981307 cites W2052677587 @default.
- W4323981307 cites W2053522428 @default.
- W4323981307 cites W2060705109 @default.
- W4323981307 cites W2070806132 @default.
- W4323981307 cites W2085259708 @default.
- W4323981307 cites W2104693064 @default.
- W4323981307 cites W2130338776 @default.
- W4323981307 cites W2162142896 @default.
- W4323981307 cites W2282821441 @default.
- W4323981307 cites W2299154667 @default.
- W4323981307 cites W2470595324 @default.
- W4323981307 cites W2555109643 @default.
- W4323981307 cites W2603298178 @default.
- W4323981307 cites W2605849493 @default.
- W4323981307 cites W2817817898 @default.
- W4323981307 cites W2902055148 @default.
- W4323981307 cites W2922353426 @default.
- W4323981307 cites W2963355447 @default.
- W4323981307 cites W3000120900 @default.
- W4323981307 cites W3013632913 @default.
- W4323981307 cites W3101158078 @default.
- W4323981307 cites W3107527779 @default.
- W4323981307 cites W3135400260 @default.
- W4323981307 cites W3176877288 @default.
- W4323981307 cites W4210870577 @default.
- W4323981307 doi "https://doi.org/10.1101/2023.03.09.531924" @default.
- W4323981307 hasPublicationYear "2023" @default.
- W4323981307 type Work @default.
- W4323981307 citedByCount "0" @default.
- W4323981307 crossrefType "posted-content" @default.
- W4323981307 hasAuthorship W4323981307A5001760759 @default.
- W4323981307 hasAuthorship W4323981307A5008597409 @default.
- W4323981307 hasAuthorship W4323981307A5052217203 @default.
- W4323981307 hasAuthorship W4323981307A5055295290 @default.
- W4323981307 hasAuthorship W4323981307A5074868327 @default.
- W4323981307 hasBestOaLocation W43239813071 @default.
- W4323981307 hasConcept C119857082 @default.
- W4323981307 hasConcept C124101348 @default.
- W4323981307 hasConcept C132525143 @default.
- W4323981307 hasConcept C153083717 @default.
- W4323981307 hasConcept C154945302 @default.
- W4323981307 hasConcept C2781067378 @default.
- W4323981307 hasConcept C41008148 @default.
- W4323981307 hasConcept C80444323 @default.
- W4323981307 hasConceptScore W4323981307C119857082 @default.
- W4323981307 hasConceptScore W4323981307C124101348 @default.
- W4323981307 hasConceptScore W4323981307C132525143 @default.
- W4323981307 hasConceptScore W4323981307C153083717 @default.
- W4323981307 hasConceptScore W4323981307C154945302 @default.
- W4323981307 hasConceptScore W4323981307C2781067378 @default.
- W4323981307 hasConceptScore W4323981307C41008148 @default.
- W4323981307 hasConceptScore W4323981307C80444323 @default.
- W4323981307 hasLocation W43239813071 @default.
- W4323981307 hasOpenAccess W4323981307 @default.
- W4323981307 hasPrimaryLocation W43239813071 @default.
- W4323981307 hasRelatedWork W2605281151 @default.
- W4323981307 hasRelatedWork W3006943036 @default.
- W4323981307 hasRelatedWork W3012234327 @default.
- W4323981307 hasRelatedWork W3023163568 @default.
- W4323981307 hasRelatedWork W3119715496 @default.
- W4323981307 hasRelatedWork W3191046242 @default.
- W4323981307 hasRelatedWork W4205364923 @default.
- W4323981307 hasRelatedWork W4206534706 @default.
- W4323981307 hasRelatedWork W4229079080 @default.
- W4323981307 hasRelatedWork W4294031299 @default.
- W4323981307 isParatext "false" @default.
- W4323981307 isRetracted "false" @default.
- W4323981307 workType "article" @default.