Transformer with convolution and graph-node co-embedding: An accurate and interpretable vision backbone for predicting gene expressions from local histopathological image
Xiao X, Kong Y, Li R, Wang Z, Lu H. Transformer with convolution and graph-node co-embedding: An accurate and interpretable vision backbone for predicting gene expressions from local histopathological image. Medical Image Analysis 2023, 91: 103040. PMID: 38007979, DOI: 10.1016/j.media.2023.103040.Peer-Reviewed Original ResearchMeSH KeywordsGene ExpressionHumansImage Processing, Computer-AssistedNeural Networks, ComputerPhenotypeConceptsHistopathological imagesVision backbonesGlobal featuresCombination of convolutional layersEncoding of local featuresGraph neural networksHistopathological image analysisGPU consumptionTransformer encoderConvolutional layersGraph-nodesLocal featuresNeural networkMinimal memoryCo-embeddingLow interpretabilityPersonal computerPathological imagesData modelSlide imagesHealth applicationsSuperior accuracyModel complexityInformation predictionHistological imagesCis-meQTL for cocaine use-associated DNA methylation in an HIV-positive cohort show pleiotropic effects on multiple traits
Cheng Y, Justice A, Wang Z, Li B, Hancock D, Johnson E, Xu K. Cis-meQTL for cocaine use-associated DNA methylation in an HIV-positive cohort show pleiotropic effects on multiple traits. BMC Genomics 2023, 24: 556. PMID: 37730558, PMCID: PMC10510240, DOI: 10.1186/s12864-023-09661-2.Peer-Reviewed Original ResearchConceptsDNA methylationMultiple traitsPleiotropic effectsGenetic variantsAberrant DNA methylationPhenome-wide association studyCis-meQTLsComplex traitsRelevant traitsDNAm sitesEnrichment analysisMeQTLsAssociation studiesSignificant traitsTraitsImmune pathwaysMethylationNew insightsMendelian randomizationImmunological functionsGenesVariantsCausal rolePathwayCpG
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