Joint modeling of mixed outcomes using a rank-based sparse neural network
Xue J, Xu Y, Li J, Ma S, Fang K. Joint modeling of mixed outcomes using a rank-based sparse neural network. Journal Of Biomedical Informatics 2025, 169: 104870. PMID: 40623577, PMCID: PMC12306493, DOI: 10.1016/j.jbi.2025.104870.Peer-Reviewed Original ResearchSparse neural networksNeural networkCompetitive performanceImbalance issueLoss functionSparse layerLeverage informationPrediction accuracyTraditional methodsNetworkParametric frameworkPenalization methodFaces challengesJoint modelPrediction modelInformationSkin cutaneous melanomaHigh-throughput profilingHigh-dimensional covariatesDimensionalityGenomic researchFeaturesMethodSimulation studyBiomedical studiesSubgroup Analysis of Differential Networks with Latent Variables
Li L, Ma S, Zhang Q. Subgroup Analysis of Differential Networks with Latent Variables. Statistics And Computing 2025, 35: 140. DOI: 10.1007/s11222-025-10681-z.Peer-Reviewed Original ResearchLow-rank structureSubgroup networksBaseline networkCompetitive performanceDifferential networksReal-world observational dataLatent variablesEfficient computational algorithmNetworkSparsityHeterogeneity analysis methodComputational algorithmInfluence of latent variablesDense networkSubgroup structureStatistical propertiesAlgorithmNetwork analysisSimulation studyMethodAnalysis methodDifferential network analysis
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